Trusted data depends on pipelines with tests, ownership, and documentation — not jobs that only the last person who touched them understands. We place data engineers who build and operate production pipelines your analytics and product teams can rely on.
Our approach
We match engineers to your warehouse, orchestration, and streaming stack. Screening covers production ownership, data quality habits, and clear handoff practices so new pipelines do not become tribal knowledge. You interview candidates aligned to scope and start date.
Skills we staff
Profiles are matched to your pipeline and warehouse tools.
- Apache Airflow — DAGs, retries, backfills, and job ownership
- Apache Spark — Batch transforms and job cost tuning
- dbt — Models, tests, docs, and CI checks
- Snowflake / BigQuery — Warehouse modeling and query performance
- Kafka & schema registry — Streaming ingestion and schema changes
- Python pipelines — Python jobs that fit your data platform
- Great Expectations — Data tests on critical pipeline runs
- Delta Lake / Apache Iceberg — Lakehouse tables, partitions, retention
- Fivetran & Airbyte — Ingestion connectors with clear owners
Outcomes teams see
- 5+ years — Average production data-engineering experience
- 2–4 weeks — Typical time to first trusted dataset
- 50%+ — Typical reduction in failed pipeline runs
- Lineage documented — New pipelines ship with source-to-consumer notes
Next step
Share your warehouse, orchestration tools, and urgency. We align engagement model and send data engineers ready to own production pipelines.