Data Engineer jobs in Israel

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Description We are looking for a talented and experienced Mid-Level Data Engineer with a passion for data and cloud technologies to join our team. In this role, you will be a key player in designing, developing, and maintaining our AWS and Databricks-based data platform. You will bridge the gap between robust Data Engineering, Database Administration (DBA), and high-performance Analytics Engineering. The ideal candidate understands the Lakehouse architecture, has experience managing modern cloud data warehouses like Snowflake, and possesses the ability to build scalable ETL pipelines that drive data-driven business decisions. Key Responsibilities • End-to-End Pipelines: Develop and maintain complex ETL/ELT pipelines using Python/PySpark on the Databricks platform, as well as loading and transforming data within Snowflake. • Data Architecture: Implement and manage data layers following the Medallion architecture (Bronze, Silver, Gold) using Delta Lake. • Optimization & Analytics: Set up and manage Databricks SQL Warehouses, perform query optimization, and utilize internal visualizations for rapid data exploration. • Cloud Infrastructure: Leverage AWS cloud services to manage data storage, compute resources, and secure data movement. • DBA & Performance Tuning: Act as a custodian for our data platform—managing indexing, clustering, vacuuming, and performance tuning across both Delta Lake and relational/warehouse environments to ensure cost-efficiency and high speed. • Data Modeling: Design data models (Star Schema / Snowflake) in the Gold layer to ensure optimal performance for BI tools (e.g., Power BI, Tableau). • Data Governance: Manage metadata, permissions, and lineage using Unity Catalog and platform-specific access controls. • Quality Control: Implement automated Data Quality tests as an integral part of the CI/CD data pipelines. Requirements • 5+ years of experience as a Data Engineer or Data Engineer/DBA – Must. • Strong hands-on experience with the AWS ecosystem (S3, IAM, EC2, etc.) and modern cloud data warehouses, specifically Snowflake. • At least 1 year of intensive, hands-on experience with the Databricks platform (including Notebooks and Workflows). • High proficiency in PySpark (or Spark Scala). • Expertise in writing complex SQL (Window Functions, CTEs) alongside DBA-level performance tuning (query profiling, indexing, partition pruning). • Technical Knowledge: Practical experience with Delta Lake, file formats (Parquet), and working with Cloud Storage (AWS S3 / Azure ADLS). • Modeling: Proven experience in designing Fact and Dimension tables. Advantages (Nice to Have) • Experience with Delta Live Tables (DLT) or dbt (Data Build Tool) on Databricks or Snowflake. • Familiarity with MLOps concepts and MLflow. • Official AWS, Snowflake, or Databricks Certifications. • Experience with external orchestration tools (e.g., Apache Airflow). Soft Skills • Ability to work independently and lead tasks end-to-end in an Agile environment. • Strong troubleshooting skills for complex, distributed processes and database bottlenecks. • Excellent communication skills and the ability to collaborate with various stakeholders (Analysts, Product Managers, and Data Scientists). AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Summary Integration into the development team for a core DWH project and various AI projects. Responsibilities include the ingestion of large volumes of new data, followed by deep understanding and analysis of data in close collaboration with Data Scientists. Design, development, and optimization of critical, diverse, and large-scale data pipelines – in both cloud and on-premise environments. Key Responsibilities: • Take responsibility for the ingestion of large volumes of new data. • Conduct deep understanding and examination of data in close collaboration with Data Scientists. • Design and develop critical, diverse, and large-scale data pipelines and processes. • Deploy and optimize data solutions across both cloud and on-premise environments. Requirements • B.Sc. in Computer Science, Engineering, Mathematics, or Statistics - Mandatory. • 3+ years of experience as a Data Engineer. • 3+ years of development experience in Python - Mandatory. • Hands-on experience with Object-Oriented Programming (OOP). • Hands-on experience with Spark for large-scale data processing - Mandatory. • Hands-on experience with dbt. • Experience building and maintaining data pipelines using tools like Airflow or Kubeflow. • Practical experience and understanding of Docker, and container platforms such as ECS / Kubernetes. • Understanding of optimization techniques and working with advanced data formats, emphasizing Parquet and Iceberg (as well as Avro, HDF5, Delta Lake). • Hands-on experience with AWS data services (including Athena, Glue, EMR / EMR Serverless, Redshift, RDS) - Significant advantage. • Experience working with and integrating BI tools such as QuickSight, Tableau, and PowerBI. • Understanding of Machine Learning concepts and processes. • Practical knowledge of GenAI / prompt engineering solutions - Advantage. • initiative and independence, team player. • Self-learning ability, high analytical skills. • Ability to work under pressure, high level of responsibility and commitment. • Working Interfaces: Development team, production team, end-users. Why work for us? Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development. We offer competitive salaries, bonus incentives, benefits, flexible hours, and mentoring. Apply now to become part of the team. AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

At Lusha, we're building for builders. We build fast and AI-first, so we look for builders. By a builder, we mean someone who turns "maybe" into "done". We're looking for a Senior Data Platform Engineer to join the Data Platform team, which owns the infrastructure every data team at Lusha runs on and sits between DevOps and the Data groups. You'll be the Databricks admin (Unity Catalog, compute, governance, cost), the Confluent (Kafka) and CDC admin, and own Airflow, Elasticsearch, the databases, the data S3 estate, and the Kubernetes cluster underneath. Your job is to make the platform fast, cheap, governed, and boring: if it's broken, you fix it, if it's manual, you automate it. Hands-on experience using AI coding tools daily, and building or integrating LLM and agent tooling into real workflows, is a must for this role. This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office. This might be for you if: You enjoy designing, building, and running large scale batch and streaming data pipelines in production, and owning what you ship, including when it breaks You like administering and evolving core platforms: Databricks, Confluent Kafka and CDC pipelines, Airflow, and Elasticsearch You care about reliability, performance, and cost of a shared data platform used across R&D You enjoy setting the engineering standard for data work: testing, observability, cost tagging, documentation You like driving architecture decisions across Databricks, Kafka, Elasticsearch, and AWS You actively use AI tools and build self-service tooling, including MCP servers and agent interfaces, so teams can operate the platform safely Requirements 5+ years hands-on building and operating large scale data pipelines, batch and streaming, in production Deep Spark and Databricks experience: Delta Lake, Unity Catalog, job and cluster tuning, Structured Streaming or DLT Streaming and CDC experience: Kafka (Confluent a plus), Debezium or equivalent, schema evolution and its failure modes Experience writing Airflow DAGs and operating the platform itself Expert Python and SQL, clean, tested, performant Strong data modeling and architecture judgment, with the ability to explain tradeoffs in scale, performance, and cost Infra literacy: comfortable with AWS (S3, IAM, networking basics), Terraform, Docker and Kubernetes, and CI/CD Proven experience leading cross-team technical initiatives end to end, from ambiguous ask to shipped and adopted Proven experience using AI coding tools daily, with LLM and agent tooling (MCP, embeddings, vector search) built into real workflows Nice to have: Elasticsearch at scale: indexing pipelines, cluster operations, reindex and rollout strategies FinOps or cloud cost management for data platforms Vault or similar secrets management, and Kubernetes operations beyond deploying your own service Experience inheriting a system with real debt and making it boring

Description In your role as a Data Engineer at Tikal, you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies. Responsibilities: • Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT. • Develop Data pipelines to ingest and transform data. • Manage and enhance orchestrated pipelines across company • Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data. • Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads. • Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking. • Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage. Requirements • 7 years of experience in Python (+Java / Scala) • Proven experience working in an AI-assisted development environment, defining high-level technical goals and leveraging autonomous AI agents (e.g., Cursor, Copilot, Devin-style tools) to execute multi-file implementations, while maintaining ownership of architecture, design decisions, code quality, and final validation. • Experience in Building data lakes, including ingestion and transformation (DBT) • Data Warehousing & ETL Pipelines • SQL & Advanced Query Optimization • Experience in data formats (Parquet, Avro, ORC) • Experience in streaming (Spark Streaming, Flink, Kafka Streams, Beam, etc.) • Experience in NoSQL and data storage (e.g. Elastic, Redis, MongoDB, CouchBase, BigQuery, Snowflake, Databricks) • Experience in messaging (Kafka, RabbitMQ, etc.) • Experience in cloud platforms (AWS, GCP)

We are making the future of Mobility come to life starting today. At Autofleet we support the world’s largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization. At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping. As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform—supporting real-time optimization, advanced analytics, and machine learning applications. What You'll Do • Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools. • Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets. • Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle. • Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows. • Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability. • Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions. • Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability. Requirements • 4+ years in backend data engineering or infrastructure-focused software development. • Proficient in Python, with experience building production-grade data services. • Solid understanding of SQL • Proven track record designing and operating scalable, low-latency data pipelines (batch and streaming). • Experience building and maintaining data platforms, including lakes, pipelines, and developer tooling. • Familiar with orchestration tools like Airflow, and modern CI/CD practices. • Comfortable working in cloud-native environments (AWS, GCP), including containerization (e.g., Docker, Kubernetes). • Bonus: Experience working with GCP • Bonus: Experience with data quality monitoring and alertingֿ • Bonus: Experience with Snowflake, DBT, Flink, Kafka • Bonus: Strong hands-on experience with Spark for distributed data processing at scale. • Degree in Computer Science, Engineering, or related field.

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships.. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com. Responsibilities • Provide the direction of our data architecture. Determine the right tools for the right jobs. We collaborate on the requirements and then you call the shots on what gets built. • Manage end-to-end execution of high-performance, large-scale data-driven projects, including design, implementation, and ongoing maintenance. • Optimize and monitor the team-related cloud costs. • Design and construct monitoring tools to ensure the efficiency and reliability of data processes. • Implement CI/CD for Data Workflows Requirements • 5+ Years of Experience in data engineering and big data at large scales. - Must • Extensive experience with modern data stack - Must: 1. Snowflake, Delta Lake, Iceberg, BigQuery, Redshift 2.Kafka, RabbitMQ, or similar for real-time data processing. 3.Pyspark, Databricks • Strong software development background with Python/OOP and hands-on experience in building large-scale data pipelines. - Must • Hands-on experience with Docker and Kubernetes. - Must • Expertise in ETL development, data modeling, and data warehousing best practices. • Knowledge of monitoring & observability (Datadog, Prometheus, ELK, etc) • Experience with infrastructure as code, deployment automation, and CI/CD. • Practices using tools such as Helm, ArgoCD, Terraform, GitHub Actions, and Jenkins. Our stack: Azure, GCP, Databricks, Snowflake, Airflow, Spark, Kafka, Kubernetes, Neo4J, AeroSpike, ELK, DataDog, Micro-Services, Python, SQL Your stack: Proven strong back-end software engineering skills, ability to think for yourself and challenge common assumptions, commitment to high-quality execution, and embrace collaboration

The world of payment processing is rapidly evolving, and businesses are looking for loyal and strategic partners, to help them grow. Meet Nuvei, Nuvei is the global fintech building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, banking, risk, and fraud management services. Connecting businesses to their customers in more than 200 markets, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights for customers and partners to succeed locally and globally through one integration. At Nuvei, we live our core values, and we thrive on solving complex problems. We’re dedicated to continually improving our product and providing relentless customer service. We are always looking for exceptional talent to join us on the journey! Your Mission Nuvei is looking for a hands-on Data Platform Engineer to operate and evolve our enterprise Kafka ecosystem, which serves as the company’s central data streaming platform. The role is critical to our data infrastructure and supports a major ongoing migration to the cloud, while covering production operations, troubleshooting, automation, maintenance, and reliability across high-volume, low-latency, 24/7 on-premises and cloud environments. Responsibilities • Operate and monitor Kafka environments across Confluent Cloud, Confluent Platform, and Kubernetes/CFK deployments, including availability, consumer lag, replication, throughput, latency, storage, and capacity. • Troubleshoot production incidents, participate in on-call, perform root-cause analysis, and improve resilience, failover, and disaster-recovery readiness. • Manage topics, partitions, retention, ACLs, consumer groups, Kafka Connect connectors, and Schema Registry. • Deploy, configure, upgrade, patch, and scale platform components and connectors while maintaining secure access and configuration consistency. • Automate platform operations and provisioning using scripting, infrastructure-as-code, and CI/CD. • Work with DevOps, SRE, Infrastructure, Security, Architecture, Data, and application teams to onboard workloads and continuously improve platform reliability and governance. Qualifications • 2+ years of hands-on experience with Apache Kafka with strong knowledge of topics, partitions, replication, producers, consumers, and consumer groups. • Hands-on experience with Confluent Platform and/or Confluent Cloud, including Kafka Connect and Schema Registry. • Experience supporting production systems on Linux, including monitoring, logs, networking, certificates, authentication, and access control. • Scripting and automation experience using Python, Bash, PowerShell, or similar tools, plus familiarity with Git and CI/CD. • Strong ownership, troubleshooting, and communication skills, with a willingness to participate in an on-call rotation. Preferred Qualifications • Experience with Microsoft Azure, Kubernetes / Confluent for Kubernetes (CFK), Helm, or Terraform. • Experience with the Kafka Connect ecosystem, including Debezium plugins, SMTs, Replicator, and source/sink connectors. • Familiarity with observability, SRE practices, SLIs/SLOs, incident management, and disaster recovery. • Experience in payments, fintech, banking, or another regulated environment; Confluent/Kafka certification is a plus. Nuvei is an equal-opportunity employer that celebrates collaboration and innovation and is committed to developing a diverse and inclusive workplace. The team at Nuvei is comprised of a wealth of talent, skill, and ambition. We believe that employees are happiest when they’re empowered to be their true, authentic selves. So, please come as you are. We can’t wait to meet you. Benefits • Private Medical Insurance • Office and home hybrid working • Global bonus plan • Volunteering programs • Prime location office close to Tel Aviv train station Our recruitment process may use automated tools, including AI, to support application management and candidate shortlisting.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Summary Integration into the development team for a core DWH project and various AI projects. Responsibilities include the ingestion of large volumes of new data, followed by deep understanding and analysis of data in close collaboration with Data Scientists. Design, development, and optimization of critical, diverse, and large-scale data pipelines – in both cloud and on-premise environments. Key Responsibilities: • Take responsibility for the ingestion of large volumes of new data. • Conduct deep understanding and examination of data in close collaboration with Data Scientists. • Design and develop critical, diverse, and large-scale data pipelines and processes. • Deploy and optimize data solutions across both cloud and on-premise environments. Requirements • B.Sc. in Computer Science, Engineering, Mathematics, or Statistics - Mandatory. • 3+ years of experience as a Data Engineer. • 3+ years of development experience in Python - Mandatory. • Hands-on experience with Object-Oriented Programming (OOP). • Hands-on experience with Spark for large-scale data processing - Mandatory. • Hands-on experience with dbt. • Experience building and maintaining data pipelines using tools like Airflow or Kubeflow. • Practical experience and understanding of Docker, and container platforms such as ECS / Kubernetes. • Understanding of optimization techniques and working with advanced data formats, emphasizing Parquet and Iceberg (as well as Avro, HDF5, Delta Lake). • Hands-on experience with AWS data services (including Athena, Glue, EMR / EMR Serverless, Redshift, RDS) - Significant advantage. • Experience working with and integrating BI tools such as QuickSight, Tableau, and PowerBI. • Understanding of Machine Learning concepts and processes. • Practical knowledge of GenAI / prompt engineering solutions - Advantage. • initiative and independence, team player. • Self-learning ability, high analytical skills. • Ability to work under pressure, high level of responsibility and commitment. • Working Interfaces: Development team, production team, end-users. Why work for us? Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development. We offer competitive salaries, bonus incentives, benefits, flexible hours, and mentoring. Apply now to become part of the team. AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Description We are looking for a talented and experienced Mid-Level Data Engineer with a passion for data and cloud technologies to join our team. In this role, you will be a key player in designing, developing, and maintaining our AWS and Databricks-based data platform. You will bridge the gap between robust Data Engineering, Database Administration (DBA), and high-performance Analytics Engineering. The ideal candidate understands the Lakehouse architecture, has experience managing modern cloud data warehouses like Snowflake, and possesses the ability to build scalable ETL pipelines that drive data-driven business decisions. Key Responsibilities • End-to-End Pipelines: Develop and maintain complex ETL/ELT pipelines using Python/PySpark on the Databricks platform, as well as loading and transforming data within Snowflake. • Data Architecture: Implement and manage data layers following the Medallion architecture (Bronze, Silver, Gold) using Delta Lake. • Optimization & Analytics: Set up and manage Databricks SQL Warehouses, perform query optimization, and utilize internal visualizations for rapid data exploration. • Cloud Infrastructure: Leverage AWS cloud services to manage data storage, compute resources, and secure data movement. • DBA & Performance Tuning: Act as a custodian for our data platform—managing indexing, clustering, vacuuming, and performance tuning across both Delta Lake and relational/warehouse environments to ensure cost-efficiency and high speed. • Data Modeling: Design data models (Star Schema / Snowflake) in the Gold layer to ensure optimal performance for BI tools (e.g., Power BI, Tableau). • Data Governance: Manage metadata, permissions, and lineage using Unity Catalog and platform-specific access controls. • Quality Control: Implement automated Data Quality tests as an integral part of the CI/CD data pipelines. Requirements • 5+ years of experience as a Data Engineer or Data Engineer/DBA – Must. • Strong hands-on experience with the AWS ecosystem (S3, IAM, EC2, etc.) and modern cloud data warehouses, specifically Snowflake. • At least 1 year of intensive, hands-on experience with the Databricks platform (including Notebooks and Workflows). • High proficiency in PySpark (or Spark Scala). • Expertise in writing complex SQL (Window Functions, CTEs) alongside DBA-level performance tuning (query profiling, indexing, partition pruning). • Technical Knowledge: Practical experience with Delta Lake, file formats (Parquet), and working with Cloud Storage (AWS S3 / Azure ADLS). • Modeling: Proven experience in designing Fact and Dimension tables. Advantages (Nice to Have) • Experience with Delta Live Tables (DLT) or dbt (Data Build Tool) on Databricks or Snowflake. • Familiarity with MLOps concepts and MLflow. • Official AWS, Snowflake, or Databricks Certifications. • Experience with external orchestration tools (e.g., Apache Airflow). Soft Skills • Ability to work independently and lead tasks end-to-end in an Agile environment. • Strong troubleshooting skills for complex, distributed processes and database bottlenecks. • Excellent communication skills and the ability to collaborate with various stakeholders (Analysts, Product Managers, and Data Scientists). AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description Upstream delivers a cloud-based, AI-powered behavioral intelligence platform for physical AI and smart mobility. Upstream empowers customers with advanced AI-driven cybersecurity solutions, including detection and response (XDR), AI Security, quality and operational intelligence, cyber threat intel, SOC services, and more. Upstream is looking for an experienced Platform Engineer to join our core platform engineering team, focusing on developing and maintaining our AI-powered automotive data management platform. As a Platform Engineer, you will design, build, and operate scalable, production-grade data and ML platform components, ensuring high reliability and performance. You will work closely with our Data Engineering and Data Science teams to develop and enhance our Iceberg-based data platform, including a data lake, query engine, and MLOps tools, providing a solid AI-ready foundation for our automotive solutions. These solutions support millions of connected vehicles worldwide, focusing on cybersecurity (XDR), vehicle quality, and operational intelligence. You will work with technologies such as Iceberg, Trino, Prefect, GitHub Actions, Kubernetes, JupyterHub, MLflow, and dbt. This role is full-time and is Herzliya, Israel-based. Responsibilities • Design, build, and maintain scalable data pipelines to ingest and transform batch data on our data lake, enabling analytics and ML with strong data quality, governance, observability, and CI/CD. • Build and expand our foundational data infrastructure, including our data lake, analytics engine, and batch processing frameworks. • Create robust infrastructure to enable automated pipelines that will ingest and process data into our analytical platforms, leveraging open-source, cloud-agnostic frameworks and toolsets. • Develop and maintain our data lake layouts and architectures for efficient data access and advanced analytics. • Develop and manage orchestration tools, governance tools, data discovery tools, and more. • Work with other team members of the engineering group, including data architects, data analysts, and data scientists, to provide solutions using a use case-based approach that drives the construction of technical data flows. • End-to-end ownership mindset with a proactive, production-first approach Requirements • BSc/BA in Computer Science, Engineering or a related field • At least 4 years of experience with designing and building data pipelines, analytical tools and data lakes • Experience with the data engineering tech stack: ETL & orchestration tools (e.g. Airflow, Argo, Prefect), and distributed data processing tools (e.g Spark, Kafka, Presto) • Experience with Python is a must • Experience working with open-source products - Advantage • Experience working in a containerized environment (e.g. k8s) - Advantage • Development experience, using a general purpose programming language (Java, Scala, Kotlin, Go, etc.) - Advantage Upstream is an equal opportunity employer. All candidates for employment will be considered without regard to race, color, religion, sex, national origin, physical or mental disability, veteran status, or any other basis protected by applicable federal, state or local law.

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for an experienced Data Infrastructure Team Lead to lead our Data Infrastructure team. In this role, you will have formal people-management responsibility for a multidisciplinary team that builds, productionizes, and improves big data and analytics infrastructure. You will be responsible for team delivery, combining technical depth, agile leadership, and an AI-first mindset to help the team build reliable systems, improve how it works, and continuously raise its engineering standards. Location: Ramat Gan (Bursa area), Israel - Hybrid model What will you do? • Lead, manage, and develop a data infrastructure team of approximately 5-7 people. • Take responsibility for team delivery, including planning, prioritization, execution, and continuous improvement. • Lead agile ways of working, including scrum-based delivery practices. • Guide the design, productionization, and ongoing improvement of big data and analytics infrastructure. • Partner closely with cross-functional stakeholders across technical, scientific, and business interfaces. • Help the team adopt AI-first engineering practices, including automation and AI-assisted development workflows. • Drive improvements in team processes, technical quality, delivery predictability, and collaboration. • Support architectural and technical decisions across cloud infrastructure, data pipelines, orchestration, and production systems. • Create an environment where team members can grow, contribute, and improve together. Requirements Required qualifications: • 5+ years of experience leading data engineering, data infrastructure, or related engineering teams, of 5+ people - a must • Strong technical background in data infrastructure, cloud computing, and production systems - a must • Hands-on experience with data warehousing tools such as Snowflake or BigQuery, and data pipeline orchestration tools such as Dagster or Apache Airflow - a must • Experience developing in Python • Experience productionizing big data, analytics, or data platform infrastructure • Experience leading agile teams and working with scrum methodologies • Demonstrated ability to lead multidisciplinary teams and collaborate effectively across interfaces. • Track record of helping teams improve how they work, including process improvement, automation, technical quality, or delivery practices. • Strategic mindset with the ability to connect technical decisions to team and business goals. • Strong English communication skills, written and spoken, for working with an international and multilingual team. Preferred qualifications: • Experience working in a scientific domain. • Experience in biotech, immunology, life sciences, or another biology-adjacent domain. • Experience with agentic AI concepts, tools, or development patterns. • Experience helping software or data engineering teams transition toward AI-first development processes. • Familiarity with modern automation practices across engineering workflows, infrastructure, and delivery pipelines. Desired personal traits: • AI-first and automation-oriented mindset. • Strong people's leadership instincts and a genuine drive to help others improve. • Collaborative, clear, and thoughtful communication style. • High ownership, good energy, and comfort operating across disciplines. • Curious, pragmatic, and able to balance strategy with hands-on technical judgment. When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. Your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. You can read more about how we process data here: https://www.immunai.com/privacy-policy/.

Description About DoorLoop DoorLoop is property management software built for speed and the smart choice for people who take growth seriously. With offices in Miami, New York City, and Tel Aviv, we’re a global company helping property owners and managers move faster, scale smarter, and get real support, real fast. We’re proudly People First. That’s why we’re a Certified Great Place to Work, recognized by Forbes as one of America’s Best Startup Employers in both 2024 and 2025, and earn top ratings on Glassdoor. We’re growing quickly and looking for a Motion Designer to help elevate our brand in the market. Mission DoorLoop is looking for a Senior Data Engineer to co-own our data platform as its second engineer. Part of the foundation is in place: a layered dbt project on Snowflake, a semantic layer that defines our core business metrics once, and an AI data assistant that answers business questions from it. Much of it is still being designed, and you will help build it. You will take design decisions with our founding data engineer, own whole business domains end to end, and raise the bar on modeling, reliability, and review as more of the company depends on the platform. You will also build data products with AI: our AI data assistant, internal dashboards, and the data apps that come next, built AI-first and tested so their answers can be trusted. You will work directly with Finance, GTM, and CX leaders on the metrics that run the business, and with analysts who ship their own dbt changes, helping them do it well. This role is for an experienced engineer who wants to shape the foundation while it is still being built: open-minded, willing to push back on the right questions, and ready to make design decisions and own them. Responsibilities • Own core business domains end to end (GTM, CX, and Finance), from the stakeholder conversation to the model, the metric, and the dashboard or AI answer built on it • Help build the foundation: take design decisions with the founding engineer on modeling patterns, incremental strategy, ingestion design, semantic-layer structure, and warehouse cost • Lead metric-definition work with business leaders: settle contested definitions, document them, and get sign-off • Own platform reliability: testing strategy, monitoring and alerting, the incident process, and root-cause fixes • Design and build ingestion for new sources, choosing the right pattern (Fivetran, Airbyte, or custom n8n → S3 → Snowpipe pipelines) • Reconcile financial data (Stripe payments and payouts) to its source of truth, and own the checks that keep revenue numbers right • Extend the semantic layer: definitions, descriptions, and verified queries, so every tool gives the same answer • Build data products with AI: the AI data assistant that answers business questions, internal dashboards, and new data apps. Design them AI-first, and build the evaluation sets that prove their answers are correct • Set the standard for code review, and mentor the analysts who ship their own dbt changes • Use AI tools every day, and build the shared skills and workflows the data team relies on Requirements • 5+ years in data engineering or analytics engineering, including at least 2 years owning a production data warehouse or dbt project • Expert SQL and deep experience with a cloud data warehouse (Snowflake preferred), including performance and cost • You have designed and led a production dbt project: structure, conventions, testing, and CI • You have built and run ingestion pipelines, with managed tools and with your own code • You have owned data reliability: monitoring, alerting, incident response, and the fixes that follow • You have led metric-definition work with senior non-technical stakeholders, such as Finance • Strong Python for pipelines, automation, and tooling • You review other people’s code and mentor less experienced colleagues • You use AI tools (Claude, Cursor, or similar) every day, with specific workflows you can describe and defend • You want to build data products with AI, not only use AI to write code • Open-minded: you ask before you assume, and you change your mind when the evidence says so • You push back on the right questions, and you make design decisions and own them • Clear, direct communicator, in writing and in conversation Bonus Points • Built or owned a semantic layer (Snowflake Semantic Views, dbt Semantic Layer, Cube, LookML, or similar) • Payments or financial data: Stripe, revenue recognition, reconciliation, month-end close • Shipped a data product with AI: an AI assistant, text-to-SQL, an LLM feature on data, or evaluation sets for AI answers • Orchestration and automation tools (n8n, Airflow, Dagster, or similar) and AWS Benefits What We Offer • Hybrid work model (4 days in office, 1 WFH day) • Unlimited paid time off • Cibus meal card • Private health insurance subsidy • Pension, Keren Hishtalmut & Dmei Havraa • Real impact at scale — product used daily by thousands of paying customers • Certified Great Place to Work · Forbes America's Best Startup Employers 2024 & 2025 Equal Opportunity Employer & AI DoorLoop is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, marital status, pregnancy, genetic information, citizenship status, or any other characteristic protected by applicable law. We use AI-assisted tools, including assessment tools that evaluate cognitive ability and personality fit, to help evaluate applications. Human oversight is maintained and no hiring decision is made solely by an automated system. For positions that can be performed in New York City, candidates may request an alternative selection process. Applicants with disabilities may also request reasonable accommodation at any stage of the hiring process. For any of these requests please contact [email protected]. For more information about the tools we use, bias audit results, and how we collect and protect applicant personal data, please review our Applicant Privacy Notice.

Description About Quicklizard Quicklizard is a dynamic pricing platform used by leading retailers, marketplaces, and e-commerce brands worldwide. Our engine ingests sales, competitor, inventory, and cost data and turns it into real-time pricing recommendations - processing billions of records a day across multi-region pipelines that never stop running. The Role We're looking for a Data Engineer to own our data architecture end-to-end. You'll design, build, and scale the pipelines that power every pricing decision we make - from raw ingestion through our data lake to the analytics and BI layers our customers rely on. This is a hands-on leadership role: you'll set technical direction, drive architectural decisions, and mentor a team of data engineers, while still writing code and owning delivery. What You'll Do • Architect and build large-scale batch and streaming ETL/ELT pipelines • Own data lake design, table modeling, and partitioning strategy across billion-row datasets • Drive query performance and cloud cost optimization across AWS and GCP • Establish data quality, observability, and reliability standards - freshness, correctness, and SLAs • Partner with backend, product, and data science teams to expose data through internal and customer-facing APIs Our Stack Spark / EMR · Airflow · BigQuery · PostgreSQL & Aurora · Kafka · RabbitMQ · Elasticsearch · Go · Python · AWS · GCP · Kubernetes · Terraform What We're Looking For • 5+ years in data engineering, with real production experience at scale (terabytes+, billions of rows) • Deep SQL and strong distributed-processing experience (Spark or equivalent) • Strong Python and/or Go • Hands-on experience with cloud data warehouses (BigQuery, Snowflake, Redshift) and orchestration tooling • AI-first mindset - you actively work with AI coding tools (Claude Code, Cursor, Copilot) and LLM-based agents as part of your day-to-day, and look for opportunities to automate and accelerate engineering work with them • Experience building or supporting AI/LLM-driven data products - pipelines that feed models, agents, or ML systems • Product mindset: you care why the data is being used, not just that the job finished green Nice to have: streaming architectures, cost/FinOps ownership, multi-region or multi-cloud systems, e-commerce or pricing domain experience, MCP servers or agentic tooling.

Description Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles. Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year. Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally. Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics. About the Role  We're growing our Data team and looking for a Data Engineer who lives and breathes data. At Appdome, data moves fast and at scale: trillions of live threat events feeding real-time detection, risk scoring, and response. This role is about designing data so it stays fast under heavy volume: the right architecture, models, and storage choices to keep latency low and throughput high. You'll own data systems end to end, from ingest to serving, and have real influence over how we build. What You’ll Do  • Design, build, and maintain scalable, low-latency data pipelines, ETL/ELT, and infrastructure. • Architect data models, schemas, and storage layouts optimized for fast retrieval at large volume. • Build real-time / event-driven ingestion and processing for high-throughput threat and risk data. • Optimize SQL and NoSQL databases - query tuning, indexing, partitioning, cost/performance tradeoffs. • Work with big-data volumes across pipelines, data lakes, and warehouse / lakehouse layers, using Spark for large-scale processing and enrichment. • Own data observability - pipeline health, freshness, and quality. • Collaborate with software engineers, security experts, and data scientists to integrate data into Appdome's products. Requirements • B.Sc. in Computer Science, Data Engineering, or a related field. • 3+ years hands-on with large-scale data infrastructure. • Strong Python (including Pandas). • Deep SQL and NoSQL knowledge, with real performance-optimization experience. • Proven experience with real-time/low-latency systems, and the data architecture and modeling instincts that make them fast. • Comfort with big-data volumes, pipelines, and data lakes. • Experience with streaming / event-driven design (e.g., Kafka). • Spark / PySpark for large-scale processing, or a strong adjacent big-data background. • Strong problem-solving skills and a proactive, independent mindset. Advantages  • Experience with ClickHouse. • Data warehousing and lakehouse / open table formats (e.g., Apache Iceberg). • Vector databases and embeddings - storing and serving them to support AI and data-science features (e.g., pgvector). • Strong system design and data architecture sense - knows how to build the data model the right way. • Experience using AI-assisted development tools (e.g., Cursor, Claude Code) in day-to-day work. • Experience with security, fraud, or other high-volume telemetry data. • Comfort with containerized infrastructure (Docker, basic Kubernetes) and light DevOps (CI/CD, Git). • ElasticSearch, Redis, DynamoDB, or Metabase. Who you are  • Independent and self-driven - comfortable owning systems end to end. • Growth-oriented - eager to develop and take on more over time. • Collaborative - communicates well across engineering, security, and data science. • Adaptable - thrives in a fast-paced environment with a can-do attitude.

Description About the role & team Our DevOps team builds and operates the production streaming infrastructure that powers the company’s real-time data ecosystem. The team owns Apache Kafka, Kafka Connect, Schema Registry, Elasticsearch, Kibana, and the supporting Linux, Windows, Kubernetes, observability, and automation layers across hybrid cloud and on-premises environments. As a DevOps & Data Engineer, you will own platform availability, performance, upgrades, capacity, incident response, and infrastructure automation. You will operate the underlying platforms, not only develop applications or pipelines that consume them. We're a collaborative, high-impact team that values ownership, technical excellence, and continuous learning. Every engineer plays a meaningful role in designing reliable, scalable systems while contributing to a culture of knowledge sharing and teamwork. If you're passionate about distributed systems, infrastructure engineering, automation, and solving complex technical challenges—we'd love to meet you. What you'll be doing • Build, operate, and maintain our core data infrastructure platforms, including Apache Kafka, Kafka Connect, Elasticsearch, SchemaRegistry and Kibana. • Design, deploy, and monitor scalable real-time data pipelines across on-premises and cloud environments. • Troubleshoot production issues, participate in incident response, perform root cause analysis, and drive long-term reliability improvements. • Develop automation, Infrastructure-as-Code solutions, and operational tooling to improve platform efficiency and reduce manual effort. • Build and maintain internal tools, APIs, and self-service capabilities for engineering and data teams. • Collaborate closely with Software Engineers, Data Engineers, Analysts, and Platform teams to ensure reliable data delivery. • Implement and maintain monitoring, alerting, and observability solutions using Prometheus and Grafana. • Support platform upgrades, infrastructure improvements, and capacity planning initiatives. • Contribute to documentation, knowledge sharing, and engineering best practices to continuously improve operational excellence. Requirements What we're looking for Technical expertise • Hands-on experience with Apache Kafka clusters, Kafka Connect, and Schema Registry (Apache or Confluent). • Strong Linux & Windows administration, troubleshooting, shell scripting, and performance tuning skills. • Strong experience with Elasticsearch and Kibana. • Experience working with CI/CD pipelines, Git, and Infrastructure-as-Code tools such as Terraform or Ansible. • Production Kubernetes and Docker experience, including troubleshooting, storage, networking, RBAC, and resource management. • Proficiency in Python (or a similar language) for automation, infrastructure tooling, and operational services. • Experience implementing monitoring and observability solutions using Prometheus and Grafana. Knowledge • Strong understanding of distributed systems and event streaming architectures. • Solid understanding of DevOps principles, Infrastructure-as-Code, and automation practices. • Familiarity with cloud infrastructure (AWS, GCP, or Azure) and hybrid cloud/on-premise architectures. • Understanding of production operations, monitoring, incident management, and operational best practices. • Familiarity with infrastructure security, access control, and secrets management. Experience • 3+ years of experience working as Platform Engineering, SRE, DevOps, or production streaming infrastructure. • Proven experience operating and supporting production systems with a strong focus on reliability and scalability. • Experience troubleshooting distributed systems and production incidents. • Experience working in cross-functional environments alongside Software Engineers, Data Engineers, and Platform teams. • Experience building, operating, and improving data platforms and streaming pipelines in production. Nice to have • Experience with Apache Flink or other stream processing technologies. • Experience with Microsoft SQL Server (MSSQL). • Experience building self-service platforms or developer enablement solutions for engineering or data teams. What we offer: Our roles offer more than just a job; you’ll become part of the evoke family! We have created an environment where our people can thrive. Check out some of the fantastic benefits on offer: • Hybrid work model • Free parking in the building + free electric car charging • Broad collective health insurance (with options for family members and extensions) • Birthday gift + day off during your birthday month • Refer a friend - bonus or gift card • HitechZone membership • Gifts on holidays and life events • Ten Bis More about evoke We’re a business that embraces change and progress. The power behind big-name brands William Hill, 888 and Mr Green, evoke is the new name for 888 Holdings. Marking a new sense of purpose, direction and ambition for the business, there couldn’t be a more exciting time to join us as we accelerate our journey to bring even greater delight to our customers with world-class experiences. That’s the future. That’s evoke. At evoke, you’ll benefit from flexibility and a culture built on trust. We’ll give you the space to be yourself and the tools you need to protect our customers while they play. We’ll invest in your future to help you develop your unique strengths and build a career that’s right for you. Apply At evoke, we prioritize diversity, equity, and inclusion for the benefit of our company, employees, and communities. We foster a welcoming and safe workplace that values all forms of diversity and provides opportunities for growth. Sounds good? Then you belong at our place! The first step in the recruitment process is kickstarting your application, followed by an initial screening call and an interview stage. Apply today to kickstart your application with evoke!

Description At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we have pioneered the world's first fully automated suite of vehicle inspection systems. At the heart of this innovation lies our advanced AI-driven technology, representing the pinnacle of machine learning, GenAI, and computer vision within the automotive sector. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone. We are looking for an experienced Senior Data Engineer to join our Data team. In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data/analytics platforms. A day in the life and how you’ll make an impact: • Design and develop high-performance Data Pipelines and ETL processes to support diverse business needs. • Work closely with Product, Business Intelligence, Sales and other teams to integrate data solutions, ensuring seamless alignment and collaboration across functions. • Continuously improve our data analytics platforms, optimizing system performance and ensuring robust, reliable data infrastructure. • Oversee the entire data lifecycle, from infrastructure setup and data acquisition to detailed analysis and automated reporting, driving business growth through data-driven insights. • Implement robust data quality checks, monitoring mechanisms, and data governance policies to maintain data integrity and security, troubleshooting and resolving any data-related issues efficiently. Requirements • 5+ years of hands-on experience in Data Engineering, preferably in a fast-paced startup or technology environment. • Strong experience designing, building, and maintaining scalable data pipelines and ETL/ELT workflows. • Experience with modern data engineering technologies such as Airflow, Spark, Flink, Kafka, Pub/Sub, and dbt – an advantage. • Experience with cloud data warehouses such as BigQuery or Snowflake, and strong hands-on experience with public cloud environments, preferably GCP or AWS. • Hands-on experience with GenAI technologies and AI-assisted engineering tools, applying them to development, data workflows, automation, debugging, and engineering productivity. • Strong SQL expertise and hands-on experience with relational and NoSQL databases such as MySQL, Firestore, Redis, and Elasticsearch. • Strong Python development skills. • Experience with BI and visualization platforms such as Looker or Tableau. • Strong analytical and problem-solving skills, with the ability to communicate technical ideas and insights clearly to both technical and business stakeholders. • B.Sc. in Computer Science, Information Systems Engineering, Software Engineering, or a related technical field – an advantage. • Ability to work from our Tel Aviv office 4 days a week. Why UVeye: • Pioneer Advanced Solutions: Harness cutting-edge technologies in AI, machine learning, and computer vision to revolutionize vehicle inspections. • Drive Global Impact: Your innovations will play a crucial role in enhancing automotive safety and reliability, impacting lives and businesses on an international scale. • Career Growth Opportunities: Participate in a journey of rapid development, surrounded by groundbreaking advancements and strategic industry partnerships.

Description This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence. "VAST's data management vision is the future of the market."- Forbes VAST Data is the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, VAST takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud. Our success has been built through intense innovation, a customer-first mentality and a team of fearless VASTronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our company’s growth and at a pivotal point in computing history. We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems. A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions. Job Description: We’re looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure—someone who’s been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isn’t just about checking boxes on tech stacks—it’s about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions. In this role, you’ll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. You’ll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve. We want someone who’s comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels. Key Responsibilities: • Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes—designed for scale, modularity, and seamless integration across real-time and batch workloads. • Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools. • Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs. • Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams. • Integrate data validation, observability tools, and governance directly into the pipeline lifecycle. • Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark). • Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets. • Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads. • Operate and debug object store–backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads. Required Skills & Experience: • 2–4 years in software / solution or infrastructure engineering, with 2–4 years focused on building / maintaining large-scale data pipelines / storage & database solutions. • Proficiency in Trino, Spark (Structured Streaming & batch) and solid working knowledge of Apache Kafka. • Coding background in Python (must-have); familiarity with Bash and scripting tools is a plus. • Deep understanding of data storage architectures including SQL, NoSQL, and HDFS. • Solid grasp of DevOps practices, including containerization (Docker), orchestration (Kubernetes), and infrastructure provisioning (Terraform). • Experience with distributed systems, stream processing, and event-driven architecture. • Hands-on familiarity with benchmarking and performance profiling for storage systems, databases, and analytics engines. • Excellent communication skills—you’ll be expected to explain your thinking clearly, guide customer conversations, and collaborate across engineering and product teams.

The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed at Mobileye. We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms. Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale. We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on. What will your job look like: • Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team. • Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset. • Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration. • Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset. • Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria. • Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training. • Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms). All you need is: • 4+ years in data engineering or backend/software engineering with serious data work — pipelines that run in production, not just notebooks. • Strong Python and the PyData stack (NumPy, PyArrow, Pandas, DuckDB). • Some background in research, algorithms, or ML — enough that you can read a paper, understand a model's outputs, and have informed conversations with algorithm engineers. • Comfort working with vision-model outputs as data: embeddings, detection results, VLM responses. • Ability to work across team boundaries — this role lives between algorithm teams, infra teams, and our own. Nice to have: • Experience with autonomous-driving datasets or perception pipelines. • 3D geometry and camera model intuition (or the mathematical background to ramp up). • Workflow orchestration (Argo, Airflow, Kubeflow). • Vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale). • Familiarity with curation concepts (active learning, hard-example mining, distribution balancing) — useful context, not a requirement. • Exposure to LLM agents or agentic workflows for data tasks.

Join PUMA, the team developing the deep-learning perceptions solutions at the heart of Mobileye's autonomous-driving system. Our work powers advanced, large-scale multi-task models. We are looking for an ML Data Engineer to own the engineering foundations behind its development and training. You will build pipelines and tools that process images, video, geometric information, and evaluate model outputs. Working alongside deep-learning researchers, you will eliminate resource-intensive bottlenecks and reduce computation costs and iteration time. What will your job look like? • Optimize distributed data and evaluation pipelines for memory efficiency, throughput, and training performance. • Build tools for data exploration, manipulation, validation, and quality assessment. • Own model manipulation, packaging, distribution workflows, and interfaces with downstream deployment and EyeQ integration. • Coordinate data contracts and workflows with researchers, infrastructure and data teams, and internal customers across Mobileye. All you need is: • Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, or a related field from a leading university. • 4+ years of hands-on software or data-engineering experience. • Excellent Python and Linux skills with strong software-engineering foundations. • Experience with SQL, Spark, and PyArrow. • Experience optimizing distributed pipelines or compute-intensive systems for memory use and parallel execution. • Familiarity with PyTorch and ML workflows, including datasets, training, checkpoints, evaluation, and GPU computation. • Strong ownership, independent problem-solving, and cross-team collaboration skills. Nice to have: • Master's degree in a relevant field. • Experience in computer vision or autonomous-driving systems. • Familiarity with model deployment, hardware-aware ML, or specialized AI accelerators. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed at Mobileye. We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms. Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale. We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on. What will your job look like: • Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team. • Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset. • Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration. • Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset. • Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria. • Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training. • Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms). All you need is: • 4+ years in data engineering or backend/software engineering with serious data work — pipelines that run in production, not just notebooks. • Strong Python and the PyData stack (NumPy, PyArrow, Pandas, DuckDB). • Some background in research, algorithms, or ML — enough that you can read a paper, understand a model's outputs, and have informed conversations with algorithm engineers. • Comfort working with vision-model outputs as data: embeddings, detection results, VLM responses. • Ability to work across team boundaries — this role lives between algorithm teams, infra teams, and our own. Nice to have: • Experience with autonomous-driving datasets or perception pipelines. • 3D geometry and camera model intuition (or the mathematical background to ramp up). • Workflow orchestration (Argo, Airflow, Kubeflow). • Vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale). • Familiarity with curation concepts (active learning, hard-example mining, distribution balancing) — useful context, not a requirement. • Exposure to LLM agents or agentic workflows for data tasks. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!