Data Engineer, Mill

• 3-5 years of experience operating data engineering systems in production
• Have built and operated data pipelines in production using Python, SQL, and tools like dbt, Airflow, Fivetran, or similar against a cloud data warehouse (e.g., Snowflake)
• Have used Infrastructure as Code (e.g., Terraform, Pulumi) to provision and manage data infrastructure, with CI/CD discipline for pipeline and infra changes (automated testing, staged rollout, rollback)
• Experience working with transactional databases (e.g., PostgreSQL, Amazon RDS) as a data source, including understanding how OLTP systems differ from warehouse/analytical workloads
• Comfort working in a collaborative environment where data consumers are partners, not just stakeholders, and comfort moving between different types of work as priorities shift
• A bias toward action

Desirable

• As a Data Engineer at Mill, you'll build and maintain the core data infrastructure that powers analytics and product data across the company — ingestion pipelines, warehouse modeling, data quality, and the self-serve analytics platform (Hex + Snowflake) our business teams rely on.
• You'll work closely with the Senior Data Engineer owning our recommendations platform, contributing to and supporting that work as needed, with the opportunity to grow into deeper recommendation/LLM-based work over time.
• You'll partner closely with product, engineering, data analytics, and marketing teams
• Design, build, and maintain scalable data pipelines across Mill's product and operational systems
• Manage and maintain data infrastructure that powers our product and operational systems, ensuring it's reliable and ready to feed external customers and downstream analytics
• Collaborate with software engineers to instrument new product features and ensure event data flows cleanly
• Help build and maintain the self-serve analytics platform in Hex and Snowflake for internal business teams
• Help maintain the metrics, table endorsements, and business logic that analysts and stakeholders rely on
• Own data quality monitoring — build alerting, validation frameworks, and observability tooling so data issues get caught before they become business problems