Summary
✨ AI‑Generated
Take ownership of a mission-critical data lakehouse serving analytics, finance, and regulatory needs. You’ll design bronze, silver, and gold data layers, build CDC streaming pipelines, manage schema evolution, optimize storage and query performance, and establish retention and archival strategies. The role offers significant architectural ownership across a high-volume, highly regulated data environment.
Highlights
Own a large-scale data lakehouse handling millions of financial events daily, shape data architecture and governance, and work with modern streaming, cloud storage, and analytical technologies at terabyte-plus scale.
Description
We are looking for Data Engineer in Kraków, Poland who will own the company data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users.
You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.
Domain: Regulated iGaming / wallet & ledger data.
Audit-heavy: regulators, finance and analytics all consume the same tables.
Millions of financial events per day, terabyte-plus scale.
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.
Requirements:
Must-have:
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.
