What are the responsibilities and job description for the Data Engineer position at Evlo AI?
About The Role
The role owns the data infrastructure that powers analytics and product decisions across the organization - designing pipelines, modeling warehouse data, and building the layer that everything downstream depends on.
You will work with analytics engineers and data scientists to turn raw event streams, third-party APIs, and production databases into reliable, well-modeled datasets used daily across the business.
Key Responsibilities
The role owns the data infrastructure that powers analytics and product decisions across the organization - designing pipelines, modeling warehouse data, and building the layer that everything downstream depends on.
You will work with analytics engineers and data scientists to turn raw event streams, third-party APIs, and production databases into reliable, well-modeled datasets used daily across the business.
Key Responsibilities
- Design, build, and maintain batch and streaming data pipelines using Python, SQL, and Airflow (or Dagster) processing millions of events daily
- Develop and optimize data models in Snowflake or BigQuery, including dimensional models, slowly changing dimensions, and incremental materializations
- Own data quality end-to-end: implement freshness checks, schema contracts, and anomaly detection using tools like dbt tests or Great Expectations
- Build and maintain ELT/ETL integrations from sources like Postgres, Kafka, Segment, and third-party SaaS APIs into the warehouse
- Partner with analytics engineers and analysts to define metrics, semantic layers, and documentation in dbt
- Instrument and monitor pipeline reliability - SLAs, alerting, incident response - and continuously improve orchestration and cost efficiency
- Contribute to architecture decisions on storage, compute, and tooling as data volume and team scope grow
- 3–6 years of experience in data engineering, analytics engineering, or backend engineering with significant data focus
- Expert-level SQL and strong Python; deep experience with dbt and a modern warehouse (Snowflake, BigQuery, or Redshift)
- Hands-on experience with workflow orchestration tools (Airflow, Dagster, Prefect) and at least one streaming technology (Kafka, Kinesis, Flink)
- Track record of owning production pipelines with real SLAs, including debugging, backfills, and on-call rotation experience
- Solid grasp of data modeling techniques (Kimball, Data Vault, or OBT) and tradeoffs between cost, latency, and correctness
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Bonus: Experience with data cataloging/lineage tools, reverse ETL, Rust or Go for high-throughput services, or building data platforms from scratch at a startup