Summary
✨ AI‑Generated
Own the architecture and operation of a terabyte-scale data lakehouse processing millions of financial events every day. You will design layered data storage, build CDC streaming pipelines, optimize performance and cost, manage schema evolution, and establish retention and governance practices for audit-sensitive data.
Highlights
Own a large-scale data lakehouse handling millions of financial events daily, make architectural decisions across storage and governance, and build highly reliable data infrastructure used by analytics, finance, and regulatory stakeholders.
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:
3 years' experience in hands-on delivery within that architecture — pipelines, ingestion, models, monitoringLakehouse 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.
