Data Engineer

About this role

Data owns the pipelines, the warehouse and the definitions the business argues about — funnel conversion, time to hire, source quality.

Build the pipelines behind hiring analytics, and be the person who can say whether a number is trustworthy.

The hard part is not the pipeline; it is that two teams asking the same question must get the same answer. Most of the work is in the semantic layer and in being willing to say a number is not trustworthy yet.

Primary responsibilities

• Build and run the pipelines that move data from the product databases into the warehouse, and keep them running
• Own ingestion from every source that matters — application events, the transactional stores, and third-party systems
• Model the warehouse in dbt so two teams asking the same question get the same answer
• Handle the awkward parts honestly: late-arriving data, backfills, schema drift and the day a source system changes without telling anyone
• Instrument data quality — freshness, volume and distribution checks that fire before somebody presents a wrong number
• Manage orchestration in Airflow, including retries and the dependencies between jobs
• Enforce the data-protection rules in the pipeline itself: what is masked, what is retained, and what must not leave a region

Skills required

Day to day this role works with SQL, Python, Airflow and dbt.

• Three or more years building batch pipelines
• Eligible to work in Poland
• Three or more years building production data pipelines
• Strong SQL and a programming language, usually Python
• Orchestration with Airflow or a comparable scheduler, including retries and dependencies