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 decision-making across the organization — building and maintaining scalable pipelines that move hundreds of millions of events daily from production systems into the warehouse and downstream data products.
You will work directly with analytics engineers, data scientists, and product teams to turn messy source systems into reliable, well-modeled datasets. This is a hands-on engineering role: the systems built here are the foundation every dashboard, model, and metric depends on.
Key Responsibilities
The role owns the data infrastructure that powers analytics and product decision-making across the organization — building and maintaining scalable pipelines that move hundreds of millions of events daily from production systems into the warehouse and downstream data products.
You will work directly with analytics engineers, data scientists, and product teams to turn messy source systems into reliable, well-modeled datasets. This is a hands-on engineering role: the systems built here are the foundation every dashboard, model, and metric depends on.
Key Responsibilities
- Design, build, and maintain batch and streaming data pipelines using Python, SQL, and Airflow (or Dagster) processing high-volume event and transactional data
- Develop and optimize data models in Snowflake (or BigQuery/Redshift), including dimensional modeling, incremental dbt models, and cost-conscious warehouse tuning
- Own data quality end-to-end: implement validation and observability tooling (Great Expectations, Monte Carlo, or custom checks) with automated alerting for schema drift and pipeline failures
- Build and manage ingestion from third-party APIs and production databases using Fivetran, Airbyte, Kafka, or custom Python extract jobs
- Partner with analytics engineers and BI teams to design semantic layers and ensure consistent metric definitions across the organization
- Instrument, monitor, and tune pipeline performance; own SLAs for data freshness, completeness, and reliability
- Contribute to data platform decisions: infrastructure-as-code, CI/CD for data assets, and documentation of data lineage and ownership
- 3–6 years of data engineering experience building and operating production data pipelines at meaningful scale
- Expert-level SQL and strong Python; deep experience with at least one orchestration tool (Airflow, Dagster, Prefect)
- Hands-on experience with a modern cloud data warehouse (Snowflake, BigQuery, or Redshift) and dbt for transformation modeling
- Experience with streaming or event-driven architectures (Kafka, Kinesis, or Flink) a strong plus for daily operations
- Comfort working in AWS or GCP environments — Lambda, S3/GCS, IAM, and infrastructure-as-code (Terraform)
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience