Analytics Engineer jobs in Israel

ABOUT PLACER.AI: Placer.ai is transforming how organizations understand the physical world. Our location analytics platform provides unprecedented visibility into locations, markets, and consumer behavior. Placer empowers thousands of customers—from Fortune 500 companies, to local governments and nonprofits— to make smarter, data-driven decisions. What sets us apart? We've built the most advanced location intelligence platform in the market while maintaining an uncompromising commitment to privacy, proving that powerful analytics and responsible data practices can coexist. Our growth reflects the market's demand: we reached $100M in annual recurring revenue within just 6 years of launching, achieved unicorn status with a $1B+ valuation in 2022, and continue to expand rapidly as one of North America's fastest-growing tech companies. We're creating a $100B+ market opportunity, and we're just getting started. Named one of Forbes America's Best Startup Employers and a Deloitte Technology Fast 500 company, we're building a culture where innovation thrives, collaboration is the norm, and every team member contributes to reshaping how the world understands location. SUMMARY: The Custom Feeds team turns Placer's data into production-grade bespoke data products for our largest customers — tailored to specific business questions and shipped on a recurring basis. We're looking for a Senior Analytics Engineer, who is both a pipeline builder and a data researcher. You'll own custom recurring feeds end to end — from methodology for new metrics, through pipeline design, to final acceptance before delivery. You'll lead conversations with internal stakeholders (US-based customer-facing teams and colleagues across R&D) and help generalize recurring logic into reusable models, metrics and packages the whole team builds on. Ideal for a rigorous, independent thinker looking for a delivery-oriented role where impact is tangible. Responsibilities • Own custom feeds from requirements through delivery and ongoing maintenance • Develop methodologies and acceptance criteria for new metrics • Build reproducible, tested production pipelines in PySpark on Databricks, orchestrated with Airflow and Terraform • Partner with Solution Engineers and customer-facing teams on timelines, methodology and customer feedback • Build reusable components and packages; collaborate with R&D and Data Science to reuse existing datasets, models and products — and make your work reusable by others • Requirements • 5+ years in data analytics / data research with large-scale datasets • 3+ years hands-on Python and PySpark at scale • Solid applied statistics — sampling and estimation, bias and coverage, significance, uncertainty — and the ability to explain limitations to non-technical audiences • Demonstrated ownership of data quality: metrics and validations for datasets others depend on • Proficiency with AI-assisted coding (Claude Code, Cursor, Copilot), with judgment about when to trust, verify or discard output • Excellent written and spoken English; comfortable presenting to customers • Ability to own deliverables independently across multiple teams under tight deadlines • B.Sc. in a quantitative field; M.Sc. / MBA or academic research experience — advantage • Technical Skills: PySpark (must, with performance awareness), Databricks or equivalent Spark platform, Airflow in production, Terraform / IaC, Git-based collaborative development WHY JOIN PLACER.AI? • Join a rocketship! We are pioneers of a new market that we are creating • Take a central and critical role at Placer.ai • Work with, and learn from, top-notch talent • Competitive salary • Excellent benefits NOTEWORTHY LINKS TO LEARN MORE ABOUT PLACER: • Placer.ai's $100M round C funding (unicorn valuation!) • See our data in action at The Anchor • Placer.ai in the news • Video: About Placer for Commercial Real Estate • Video Playlist: Placer Brand & Explainer Videos Placer.ai is committed to maintaining a drug-free workplace and promoting a safe, healthy working environment for all employees. Placer.ai is an equal opportunity employer and has a global remote workforce. Placer.ai’s applicants are considered solely based on their qualifications, without regard to an applicant’s disability or need for #LI-AC1

About Aidoc Aidoc is the market leader in healthcare AI and has developed the world's largest clinical frontier model, supporting nearly 50 million patients each year. Aidoc is deployed in more than 2,000 medical centers worldwide. Since our founding in 2016, Aidoc has raised over $500 million and achieved a record number of FDA-cleared solutions. Medical diagnosis is hitting a breaking point, where the number of physicians hasn't kept up with the rapidly growing patient load. This leads to misdiagnosis and treatment delays that result in hundreds of thousands of deaths every year. Aidoc assists clinicians by precisely highlighting diseases directly on medical images, preventing misdiagnosis and ensuring that urgent patients receive the immediate attention they need. About this role • The Aidoc Clinical Value team plays a pivotal role in evaluating the outputs and performance of Aidoc's products to demonstrate the value delivered to customers in real-world clinical and operational settings. • We are looking for an experienced Analytics Engineer to join our Clinical Value team and develop the data foundations, infrastructure, and analytical tools that enable our work. • This hybrid role combines Analytics Engineering with hands-on data analysis. You will translate complex analytical and clinical needs into scalable, reliable, and reusable data solutions. • You will design data models, pipelines, analytical layers, and tools that enable complex Clinical Value studies across products and customers, while remaining hands-on with selected analytical projects. • You are expected to be independent, proactive, and impact-oriented, taking end-to-end ownership of technical challenges and opportunities for improvement. • evaluation. Responsibilities • Design, build, and maintain the data infrastructure supporting Clinical Value analyses, including reusable data models, pipelines, analytical layers, and tools. • Own the technical and analytical implementation of complex Clinical Value applications, adapting established methodologies to different products, customers, and real-world data environments. • Develop complex analytical solutions into reusable and scalable frameworks, enabling broader and more efficient execution across the team. • Identify data-quality issues, technical bottlenecks, and repetitive processes, and develop solutions and automation to improve reliability and efficiency. • Work closely with analysts, Data Engineering, and other technical teams to translate analytical needs into effective, maintainable data solutions and establish best practices. • Lead selected hands-on data-analysis projects, analyzing complex clinical and operational datasets to generate credible, actionable conclusions. • Collaborate with Go-to-Market teams, including Customer Success, to understand customer needs and support impactful customer-facing value demonstrations. Requirements • Education & Experience: B.Sc. or higher in a Scientific or Engineering discipline with 5+ years of experience in Analytics Engineering, Data Engineering, or a technical analytics role. • Core Technical Stack: Advanced Python and SQL skills; hands-on experience designing and building production data models, pipelines, and analytical layers. • Tools & Platforms: Experience with orchestration/transformation tools (DBT, Airflow, or similar) and cloud data platforms (Databricks, Snowflake, BigQuery, or AWS). • Engineering Standards: Strong understanding of data modeling, data quality, testing, and maintainability best practices. • AI Tooling & Automation: Hands-on experience integrating AI development tools and agents (e.g., Cursor, Claude agents, GitHub Copilot) into daily workflows to accelerate coding, debugging, and pipeline development. • Data Analysis Capabilities: Strong analytical mindset with the ability to independently explore and analyze complex datasets, summarize findings, and extract meaningful insights as needed. • Problem-Solving & Communication: Demonstrated ability to work independently, manage tight timelines, and translate ambiguous analytical needs into robust technical frameworks. Excellent communication skills for bridging technical and non-technical stakeholders. Nice to have • Prior experience or a strong passion for healthcare, AI, or the medical field. • Proven experience in project management or technical team leadership. Working at Aidoc Our Perks: • Be part of something big - using cutting-edge technologies to transform the Healthcare industry (while saving patients’ lives) • We work in a hybrid model, with our new offices located at 34 HaMasger Street in Tel Aviv and parking for employees. • Amazing and healthy breakfasts and lunches prepared daily by our personal chef! • Stocked up kitchen & meal card • Wellness: Aidoc employees-only gym, plus Pilates, Yoga, and functional workouts classes. • Amazing culture - collaborative, transparent & fun! • Attractive compensation package & benefits Aidoc is deeply committed to creating an inclusive workplace, and to the principle of equal opportunity for all individuals. We prohibit discrimination and harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law.

Description We are looking for an Analytics Engineer (contract) to join Artifactory Product Analytics and build the data platform behind how we measure our products. You will own the engineering side of analytics: designing product telemetry with PMs and R&D, modeling that data into trusted, reusable datasets in the warehouse, and making those datasets easy for analysts and stakeholders to consume. Product analysts own the product questions and metric definitions; you make the data underneath them fast, consistent, and dependable. You will work inside an AI-native analytics environment (Cursor, Claude, and similar coding agents) as the default way of shipping — not as an optional add-on. Fixed-term contract: 6–8 months. Start date: October 12, 2026. Responsibilities: • Design telemetry: partner with PMs and R&D to define event and data specs for product usage — schemas, properties, validation, and the path into the warehouse (core, ongoing part of the role) • Build the modeled layer: develop and maintain dbt transformation models (staging → intermediate → gold) so Artifactory product data is consistent, tested, documented, and reusable • Enable self-serve: with the analysts, design and implement how stakeholders reach certified product data so recurring questions don't need a custom pull • Align definitions: standardize entities, grain, and model structure across Artifactory areas together with the analysts who own product meaning • Ship in an AI-native workflow: specs, SQL, dbt models, reviews, and documentation done in Cursor / Claude and equivalent tools Requirements Must-have requirements: • 4+ years in analytics engineering, data modeling, or a closely related warehouse role — shipping production-grade models, not only ad-hoc SQL • Hands-on dbt in production: models, tests, documentation, version control • Strong SQL and warehouse data modeling (layered models, grain, entities) • Amazon Redshift or a comparable cloud warehouse (Snowflake, BigQuery) • Proven experience designing product telemetry with PMs or engineers — event schemas, properties, validation • Experience with event-tracking platforms / tracking-plan tooling (Snowplow, Segment, Amplitude or similar) • Daily, fluent use of Cursor, Claude, or equivalent agentic coding environments to write, review, and ship data work • Git-based workflows and code review • Comfortable building a shared data layer in close partnership with embedded product analysts; clear English communication with PMs and engineers Nice to have: • Snowplow specifically (strongly preferred) • Redshift specifically • BI / semantic-layer exposure (Looker, Tableau, Metabase or similar) • Python for data tooling • B2B DevOps / developer-tools product domain • Bachelor's degree in a quantitative or technical field, or equivalent hands-on experience

Description We are looking for an Analytics Data Engineer (contract) to join Artifactory Product Analytics and build the data platform behind how we measure our products. You will own the engineering side of analytics: designing product telemetry with PMs and R&D, modeling that data into trusted, reusable datasets in the warehouse, and making those datasets easy for analysts and stakeholders to consume. Product analysts own the product questions and metric definitions; you make the data underneath them fast, consistent, and dependable. You will work inside an AI-native analytics environment (Cursor, Claude, and similar coding agents) as the default way of shipping — not as an optional add-on. Fixed-term contract: 6–8 months. Start date: October 12, 2026. Responsibilities: • Design telemetry: partner with PMs and R&D to define event and data specs for product usage — schemas, properties, validation, and the path into the warehouse (core, ongoing part of the role) • Build the modeled layer: develop and maintain dbt transformation models (staging → intermediate → gold) so Artifactory product data is consistent, tested, documented, and reusable • Enable self-serve: with the analysts, design and implement how stakeholders reach certified product data so recurring questions don't need a custom pull • Align definitions: standardize entities, grain, and model structure across Artifactory areas together with the analysts who own product meaning • Ship in an AI-native workflow: specs, SQL, dbt models, reviews, and documentation done in Cursor / Claude and equivalent tools Requirements Must-have requirements: • 4+ years in analytics engineering, data modeling, or a closely related warehouse role — shipping production-grade models, not only ad-hoc SQL • Hands-on dbt in production: models, tests, documentation, version control • Strong SQL and warehouse data modeling (layered models, grain, entities) • Amazon Redshift or a comparable cloud warehouse (Snowflake, BigQuery) • Proven experience designing product telemetry with PMs or engineers — event schemas, properties, validation • Experience with event-tracking platforms / tracking-plan tooling (Snowplow, Segment, Amplitude or similar) • Daily, fluent use of Cursor, Claude, or equivalent agentic coding environments to write, review, and ship data work • Git-based workflows and code review • Comfortable building a shared data layer in close partnership with embedded product analysts; clear English communication with PMs and engineers Nice to have: • Snowplow specifically (strongly preferred) • Redshift specifically • BI / semantic-layer exposure (Looker, Tableau, Metabase or similar) • Python for data tooling • B2B DevOps / developer-tools product domain • Bachelor's degree in a quantitative or technical field, or equivalent hands-on experience