Data Engineer

🎯 What You’ll Actually Do

• Build and run scalable pipelines (batch + streaming) that power gameplay, wallet, and promo analytics.

• Model data for decisions (star schemas, marts) that Product, BI, and Finance use daily.

• Make things reliable
  : tests, lineage, alerts, SLAs. Fewer surprises, faster fixes.

• Optimize ETL/ELT for speed and cost (partitioning, clustering, late arrivals, idempotency).

• Keep promo data clean and compliant (PII, GDPR, access controls).

• Partner with POs and analysts on bets/wins/turnover KPIs, experiment readouts, and ROI.

• Evaluate tools,
  migrate or deprecate with clear trade-offs and docs.

• Handle prod issues without drama, then prevent the next one.

• 4+ years building production data systems. You’ve shipped, broken, and fixed pipelines at scale.

• SQL that sings and Python you’re proud of.

• Real experience with OLAP and BI (Power BI / Tableau / Redash — impact > logo).

• ETL/ELT orchestration (Airflow/Prefect or similar) and CI/CD for data
  .

• Strong grasp of warehouses & lakes: incremental loads, SCDs, partitioning.

• Data quality mindset: contracts,
  tests
  , lineage, monitoring.

• Product sense: you care about player impact
  , not just rows processed.

• Kafka (or similar streaming), Click House (we like it), dbt (modular ELT).

• AWS data stack (S3, IAM, MSK/Glue/Lambda/Redshift) or equivalents.

• Containers & orchestration (
  Docker/K8s
  ), IaC (Terraform).

• Familiarity with AI/ML data workflows (feature stores, reproducibility).

• iGaming context: provider metrics bets / wins / turnover
  , regulated markets, promo events.

• Speed > perfection. Iterate, test, ship.

• Impact > output. One rock-solid dataset beats five flaky ones.

• Behavior > titles. Ownership matters more than hierarchy.

• Direct > polite. Say what matters, early.

• Fully remote (EU-friendly time zones) or Bratislava if you like offices.

• Unlimited vacation + paid sick leave.

• Quarterly performance bonuses.

• No micromanagement. Real ownership, real impact.

• Budget for conferences and growth.

• Product-led culture with sharp people who care.

Python, SQL, Airflow/Prefect, Kafka, Click House/OLAP DBs, AWS (S3 + friends), dbt, Redash/Tableau, Docker/K8s, Git Hub Actions.

👉 If you know how to make data boringly reliable and blisteringly fast —
hit apply and let’s talk.