idankars is a technology company based in Tel Aviv, operating in a space where data and engineering intersect. Based on the role and team structure, the company appears to be a product-driven tech organization with an R&D function that relies on reliable data infrastructure to guide decisions. Tel Aviv's tech ecosystem suggests a collaborative, engineering-minded culture where analytical rigor is taken seriously. The team likely works across product, data, and engineering disciplines, making clean, trustworthy data a real competitive asset.
About the Role
This is an opportunity for a Junior Analytics Engineer to join idankars' R&D team in a hybrid role based in Tel Aviv, building the data models and pipelines that power real product and business decisions. You'll work at the intersection of engineering and analytics — turning raw data into reliable, well-structured datasets that stakeholders actually use. For someone early in their analytics engineering career, this role offers meaningful ownership, close collaboration with engineers and analysts, and the chance to shape data infrastructure from the ground up.
Responsibilities
Build and maintain dbt or SQL-based data models that serve as the trusted source of truth for R&D and business stakeholders making day-to-day decisions.
Write clean, documented SQL transformations that convert raw source data into analytical datasets — structured for clarity, tested for reliability, and ready for scale.
Collaborate with data and engineering teammates to ship complete data pipelines and reporting layers end-to-end, from ingestion through to consumption.
Catch and resolve data quality issues upstream — before they surface in dashboards, mislead stakeholders, or slow down product decisions.
Partner with analysts, product managers, or engineers to translate analytical needs into well-designed data models that hold up over time.
Contribute to documentation and data standards so that the team's data assets remain discoverable, understandable, and maintainable as the stack grows.
Has built and maintained data models (using tools like dbt or SQL) that stakeholders rely on for actual decisions — not just prototype work that sat unused.
Has written clean, well-documented SQL to transform raw, messy source data into reliable analytical datasets with consistent logic and clear structure.
Has shipped a data pipeline or reporting layer end-to-end in collaboration with a data or engineering team — understands what it takes to get something into production and keep it running.
Has caught and resolved data quality issues before they reached downstream consumers — proactively monitors, investigates, and fixes rather than waiting for someone to report a problem.
Communicates clearly with both technical and non-technical collaborators — can translate a stakeholder's analytical question into a well-scoped modeling task.
Treats documentation as part of the job, not an afterthought — leaves data assets better understood than they were before.
Nice to have
Has hands-on experience within a modern data stack — Snowflake, BigQuery, Redshift, dbt, or Airflow in a real project context.
Has built or maintained dashboards in a BI tool such as Looker, Metabase, or Tableau, and understands what makes a dashboard actually useful versus just pretty.
Has contributed to a data catalog or documentation practice that helped teammates find and trust data assets faster.
Has basic Python skills applied to data transformation, scripting, or pipeline automation.
Has worked in an R&D or product-focused environment and understands how engineering teams think about data needs.
Has sat in on or contributed to data model design conversations alongside analysts or product managers.
Benefits
Hybrid work model based in Tel Aviv — structured flexibility without sacrificing team collaboration.
A seat on an R&D team where your data work directly shapes product and business decisions, not a supporting-role afterthought.
Real ownership early — you'll contribute to production data infrastructure, not spend months on internal tooling no one uses.
Mentorship and close collaboration with experienced engineers and analysts who care about craft and code quality.
Opportunity to help define data modeling standards and documentation practices as the stack matures.
Competitive compensation commensurate with experience, reviewed as you grow into the role.
