Data Engineer jobs in Tel Aviv-Yafo

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

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 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 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 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.

Job Description • Architect and evolve our Data Warehouse, Data Lake, and ETL/ELT pipelines across multiple products and business domains • Design and build enterprise-grade backend services and APIs that expose data platform capabilities at scale • Own technical decisions on scalability, reliability, observability, and data quality • Lead cross-functional initiatives with R&D, product, analysts, and business stakeholders • Mentor data engineers and developers through design reviews, technical leadership, and engineering best practices • Drive adoption of modern data technologies, AI-first practices, and automation Qualifications • 6+ years in Data Engineering, including production-scale platform design and operation using Spark • 2+ years as a Tech Lead or senior engineer leading technical initiatives • Hands-on backend development experience - REST/gRPC APIs, microservices, service design patterns - at enterprise scale • Broad database fluency: relational, analytical, NoSQL, columnar, and streaming stores • Advanced SQL and strong Python; proven experience building and optimizing large-scale ETL/ELT pipelines • Experience with cloud-based data platforms and AI-driven data workflows • Practical proficiency with AI agents and tools - building, integrating, or orchestrating LLM-powered workflows in production environments • Strong communication skills with technical and non-technical stakeholders • Self-directed and forward-thinking - you bring a clear point of view, drive initiatives without being told, and see around corners on where the platform needs to go next Nice to have • Azure data services, Databricks or EventHub • Streaming and real-time data processing architectures • Background in cyber security, SaaS, or large-scale enterprise environments • Data governance, observability, or self-service platform experience #LI-NK2