Caliente Interactive: Data Engineer — Data Lakehouse Senior
Summary
Senior Data Engineer managing a large-scale data lakehouse for financial events in the iGaming sector. The role involves designing lakehouse architecture (Iceberg/Delta), implementing CDC streaming with Kafka/Debezium, and ensuring data governance and reconciliation across S3, Snowflake, and Databricks.
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.
