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 - building reliable batch and streaming pipelines that move tens of millions of events daily from source systems into a modern cloud warehouse.
The Data Engineer will work closely with analytics engineers, data scientists, and product teams to model data, enforce quality standards, and reduce the time between raw event capture and business-ready insight.
Key Responsibilities
The role owns the data infrastructure that powers analytics and product decisions across the organization - building reliable batch and streaming pipelines that move tens of millions of events daily from source systems into a modern cloud warehouse.
The Data Engineer will work closely with analytics engineers, data scientists, and product teams to model data, enforce quality standards, and reduce the time between raw event capture and business-ready insight.
Key Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines using Python, SQL, and orchestration tools such as Airflow, Dagster, or dbt Cloud
- Develop and optimize data models in Snowflake, BigQuery, or Databricks, balancing query performance, storage cost, and maintainability
- Implement data quality checks, anomaly detection, and observability tooling to catch pipeline failures and schema drift before they hit downstream consumers
- Build streaming and near-real-time ingestion pipelines using Kafka, Kinesis, or Flossom-equivalent event streaming technologies
- Partner with analytics engineers to define semantic layers, dimensional models, and documented data contracts for core business entities
- Optimize warehouse costs and pipeline performance through partitioning, clustering, incremental processing, and query tuning
- Contribute to infrastructure-as-code and CI/CD practices for data assets using Terraform, GitHub Actions, and version-controlled dbt projects
- 3-6 years of experience in data engineering, analytics engineering, or backend engineering with significant data infrastructure ownership
- Expert-level SQL and strong Python skills, including writing production-grade, tested pipeline code
- Hands-on experience with a modern cloud data warehouse (Snowflake, BigQuery, or Databricks) and orchestration tooling (Airflow, Dagster, or Prefect)
- Experience with one or more cloud platforms: AWS, GCP, or Azure, including storage (S3, GCS) and compute services
- Solid grasp of data modeling techniques - dimensional modeling, Kimball methodologies, and event-based schemas
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Bonus: Experience with streaming architectures (Kafka, Flink), data cataloging/lineage tools (dbt docs, Monte Carlo, Amundsen), or having built data platforms from early-stage maturity