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CommIT

Data Engineer — Data Lakehouse

CommIT Warsaw, Masovian Voivodeship, Poland

Software Development · 501-1,000 employees

4 h ago
data-engineer Mid (2-5 yrs) Full-time Poland
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About the role

You will own the end-to-end data lakehouse architecture, ensuring accurate data ingestion, storage, and governance for financial events. This includes managing streaming pipelines, optimizing query performance, and maintaining regulatory compliance and data quality.

What they look for

Data Engineering Lakehouse Architecture Snowflake Databricks Apache Iceberg Delta Lake Kafka Debezium Trino Athena Python Airflow Dagster SQL Data Modeling Cloud Storage

Requirements

Candidates must have at least 3 years of hands-on experience with lakehouse architectures, including schema evolution and open table formats. Proficiency in cloud data warehousing, CDC streaming, SQL, and Python is essential for this role.

Full 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, monitoring
  • 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.

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