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:
+ 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.
... project information will be shared during the recruitment process. Bachelor’s degree in Computer Science, Engineering, or related field, Proven experience as a Data Engineer or similar role, Strong knowledge of data processing technologies and database systems (SQL, NoSQL), Experience with data pipeline and workflow management ...
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