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Open nowFirst seen 44 hours ago

Data Engineer

comm-it/76.008133 open roles

Where
Poland
Work mode
Hybrid
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Your applicationOpen nowData Engineercomm-it/76.008 · Poland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. comm-it/76.008 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: first seen 44 hours ago

comm-it/76.008 median: 1 days open

The posting

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:

  • 3+ years 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.

Nice to have:

  • Fintech, iGaming, or another regulated, audit-heavy environment.
  • Cost monitoring / FinOps for storage and compute spend.
  • Hudi specifically; Dagster specifically.
  • Immutability / WORM regulatory retention.
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