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 business - batch and streaming pipelines, warehouse modeling, and the reliability of the data platform end-to-end.
You will partner closely with data analysts, analytics engineers, and backend teams to turn raw event, transactional, and third-party data into trustworthy, well-modeled datasets used daily across the organization.
Key Responsibilities
The role owns the data infrastructure that powers analytics and product decisions across the business - batch and streaming pipelines, warehouse modeling, and the reliability of the data platform end-to-end.
You will partner closely with data analysts, analytics engineers, and backend teams to turn raw event, transactional, and third-party data into trustworthy, well-modeled datasets used daily across the organization.
Key Responsibilities
- Design, build, and maintain ETL/ELT pipelines in Python and SQL, orchestrating workflows with Airflow or Dagster
- Develop and optimize data models in Snowflake (or similar cloud warehouse), including dimensional models, incremental loads, and data contracts
- Build and scale ingestion pipelines for streaming and batch sources using Kafka, Fivetran, or custom Python connectors
- Implement data quality and observability frameworks - freshness checks, anomaly detection, and lineage tracking - to keep downstream analytics trustworthy
- Partner with analytics engineers to refine dbt models, semantic layers, and metric definitions used across BI tools
- Optimize warehouse performance and pipeline costs through query tuning, partitioning strategies, and incremental processing
- Write tested, documented, production-grade code; contribute to code reviews and infrastructure-as-code practices with Terraform
- 3–6 years of data engineering experience building and operating production pipelines at scale
- Expert-level SQL and strong Python skills, including experience with distributed processing (Spark or similar)
- Hands-on experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) and orchestration tooling (Airflow, Dagster, or Prefect)
- Experience with dbt and modern data stack tooling for transformation, testing, and documentation
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Track record of collaborating with analysts and stakeholders to translate business questions into data models
- Bonus: experience with Kafka/streaming architectures, data catalog tooling, or building data platforms in fast-growing startups