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CommIT

Middle 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 data lakehouse architecture, including designing and maintaining reliable pipelines for ingestion, storage, and governance. Your work ensures that Analytics, Finance, and Regulatory teams have access to accurate, consistent, and fully traceable data.

What they look for

Data Engineering Lakehouse Architecture AWS S3 Snowflake Databricks Kafka Debezium SQL Data Modeling Python Airflow Dagster Trino Athena Data Governance CDC

Requirements

Candidates must have at least 2 years of hands-on experience in a production lakehouse environment with expertise in open table formats and cloud data warehouses. Strong proficiency in SQL, data modeling, streaming ingestion, and cloud object storage is essential for this role.

Full description

We’re looking for a Mid Data Engineer to take ownership of our data lake — the system of record for millions of financial events every day, including bets, wallet transactions, and live odds, serving 12M+ active users. You’ll be responsible for designing and maintaining reliable data pipelines and defining how data is ingested, stored, retained, reconciled, and governed across AWS S3 and Snowflake/Databricks. Your work will ensure that Analytics, Finance, and Regulatory teams have access to accurate, consistent, and fully traceable data — with zero drift from source systems.

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

What you will do:

  • 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:

  • 2 years of hands-on in a production lakehouse environment.
  • 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.

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