Middle Data Engineer/ Data Lakehouse
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Location
Hybrid (Warsaw, Poland)
Seniority
Middle
Employment
Full-Time
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 5, 2026. Comm-IT scores B on the Alion truth index.
Overview
Company
Impact
Profile match
Comm-IT is an Israeli technology services company headquartered in Petah Tikva that provides software development, cloud and managed services, generative AI and ML, data and big data, cybersecurity, embedded and IoT, and IT and communication infrastructure work. Founded in 2005 by veterans of IDF technology units, it reports about 1,000 experts and 1,200 clients, with an office in New York and delivery centers in Ukraine, Poland, Armenia, Bulgaria and other countries. It hires backend, full-stack and React developers, DevOps and data engineers, GCP specialists, help desk and network technicians, project and product managers and QA engineers.
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 - withzero 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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