Middle Data Engineer/ Data Lakehouse

Commit; Województwo mazowieckie; Hybrid; PLN 120,000 - 180,000; Full time

Job summary

Commit is seeking a Mid Data Engineer to own our data lake, the system of record for millions of financial events daily. You will design reliable data pipelines, govern data, and optimize performance across Snowflake/Databricks, S3, and related tools. Location: Kraków, Poland.

Hybrid work two days per week from the office. You’ll work with Analytics, Finance, and Regulatory teams to ensure accurate, traceable data with minimal drift from source systems, while managing GDPR retention and data

Qualifications

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

Responsibilities

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.

Skills

Lakehouse architecture

SQL

Data modeling

CDC & streaming ingestion

Cloud data platforms

Python

Airflow or Dagster

Snowflake

Databricks

Trino/Athena/Snowflake performance

Kafka/Debezium

Governance & GDPR

Tools

Snowflake

Databricks

BigQuery

Iceberg/Delta/Hudi

Kafka

Debezium

Airflow

Dagster

Trino

Athena

S3

Job 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.