What is a Data Engineer?
A data engineer builds and maintains the pipelines, warehouses, and infrastructure that move data from where it's generated to where it's useful. They're the foundation of a company's analytics capability — without reliable data pipelines, data scientists can't analyze, analysts can't report, and machine learning engineers can't train. At a startup, data engineers often own the entire data stack: ingestion, transformation, warehousing, and observability.
At what stage should you hire a Data Engineer?
Series A through Series B, once your product is generating enough event data that analytics is a meaningful function and the current ad hoc approach (manual SQL pulls, brittle scripts) is slowing the team down. The prerequisite: you have a destination for the data (Snowflake, BigQuery, Redshift) and a business reason to query it reliably. Without either, you're building infrastructure for its own sake.
Common titles for this role
- Data Engineer
- Analytics Engineer (more transformation-focused)
- Data Infrastructure Engineer
- Senior Data Engineer
- Data Platform Engineer
- ETL Engineer
What does a Data Engineer do at a startup?
- Build and maintain data ingestion pipelines: Fivetran, Airbyte, custom connectors, Kafka
- Design and maintain the data warehouse schema: Snowflake, BigQuery, or Redshift
- Build transformation pipelines using dbt or equivalent tools
- Implement data quality monitoring and alerting
- Instrument product event tracking: Segment, Amplitude, or custom event pipelines
- Enable self-serve analytics: clean, documented, queryable data models
- Manage data infrastructure costs and optimize query performance
Key skills and qualifications
- Strong SQL expertise — this is the core language of data engineering
- Python for pipeline development and automation
- Experience with data warehouse platforms: Snowflake, BigQuery, or Redshift
- Pipeline orchestration: Airflow, Prefect, Dagster, or dbt Cloud
- Data ingestion tools: Fivetran, Airbyte, or custom connector development
- Understanding of streaming vs. batch processing tradeoffs
Why hire your Data Engineer through RFS?
- Data engineering is a specialized search — we screen for hands-on pipeline and warehouse experience, not just SQL familiarity
- 29-day average time to hire — data engineering searches benefit from a pre-vetted candidate pool
- 300+ placements at VC-backed companies across data and engineering functions
- Pre-vetted for the modern data stack: dbt, Snowflake, Airflow experience confirmed before you see a resume
- No upfront fees
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