Data Engineer

Pentasia; Kraków; On-site; PLN 180,000 - 280,000; Full time

Job summary

Pentasia seeks a data engineering professional to own the lakehouse architecture across S3, Snowflake, and Databricks. You will land, store, retain, and govern millions of financial events daily, ensuring accuracy and zero drift from source.

You will implement CDC streaming (Kafka/Debezium), design partitioning and file layouts for cost and speed, and enforce governance, retention, and security policies. This is an architecture-focused role requiring strong SQL, Python, and orchestration

Qualifications

• 3+ years in data engineering with ownership of a large-scale data lake.
• Experience with production Snowflake/Databricks or BigQuery in the cloud.
• Hands-on with Iceberg/Delta/Hudi open table formats.
• CDC/streaming ingestion experience (Kafka + Debezium)
• Strong SQL and data modeling for financial ledgers.
• Data lake governance: catalogs, lineage, access control, PII retention.

Responsibilities

• Own lakehouse architecture and table formats.
• Land operational data via CDC streaming.
• Design data layout for speed and cost.
• Manage retention, archiving, GDPR deletion.
• Ensure data freshness and reconcile against source.
• Oversee governance: catalog, lineage, access controls.
• Monitor ingestion health and cloud spend.

Skills

SQL

Python

Data modeling

Data governance

Cloud data lake

CDC streaming

Tools

Snowflake

Databricks

S3

Iceberg

Delta

Kafka

Debezium

Airflow

Dagster

Job description

You’ll own the data lake — the system of record for millions of financial events a day across million of active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

What you'll be doing

• 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.

What we're looking for

• 3+ years in data engineering, with real ownership of a large-scale data lake or lakehouse.
• Cloud object storage (S3/GCS) plus Snowflake, Databricks, or BigQuery in production.
• Hands-on with an open table format (Iceberg, Delta, or Hudi).
• Deep understanding of partitioning, file layout, and query optimization at terabyte-plus scale.
• CDC and streaming ingestion experience (Kafka + Debezium or equivalent).
• Strong SQL and data modeling; enough relational grounding to reason about the OLTP systems you capture from — critical for financial ledgers.
• Data lake governance: catalogs, lineage, access control, PII, retention.
• Python and an orchestrator (Airflow/Dagster) as tools — this is an architecture and ownership role, not heads-down coding.
• Nice to have: fintech, iGaming, or another regulated, audit-heavy environment.