Data Engineer — Data Lakehouse (Hybrid, Kraków, Poland)

What you'll be doing

Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.

Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.

Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.

Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.

Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.

Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.

Monitor ingestion health, data anomalies, and cloud storage/compute spend.

Must-have

Senior: 5+ years in data engineering, with real ownership of a large-scale data lake or lakehouse.

Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.

Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.

Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.

CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.

Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.

Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.

Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.

Cloud object storage — S3 or GCS, plus storage tiering and archival.

Python and an orchestrator — Airflow or Dagster, as tools.

Location & work model

Kraków, Poland. Hybrid — 2 days per week from the office.