Data Engineer

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.