Data Engineer

The Role

As a Data Engineer at Mill, you ll touch systems end-to-end — from raw ingestion to the recommendation a customer sees in the app to managing the data warehouse. You ll architect a warehouse model one week and tune recommendation logic the next. 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
• Build and operate the customer-facing recommendation engine — including LLM-based logic where useful — that turns characterized food waste data into actionable recommendations: purchasing suggestions, anomaly explanations, operational nudges
• Design transformation and integration pipelines for food data coming from multiple sources — including agent-based reconciliation where it helps — handling schema changes, validation, and consistency issues
• Partner with data analytics and marketing teams to support self-serve analytics tools
• Own data quality monitoring — build alerting, validation frameworks, and observability tooling
• Bring CI/CD discipline to pipeline — automated tests, staged rollouts, and rollback paths — and track recommendation accuracy over time so we know whether a change actually helped
• Define and maintain the metrics, table endorsements, and business logic that analysts and stakeholders rely on — so everyone across the company is working from the same numbers

• 5 years of experience operating data engineering systems in production
• Have built and operated data pipelines in production using Python and tools like dbt, Airflow, Fivetran, or similar — including handling failures, backfills, and schema changes after launch
• Strong SQL skills and experience with a cloud data warehouse (e.g., Snowflake, Big Query, Redshift)
• Experience with recommendation systems or pipelines that combine multiple data sources into a single product-facing output, in production — including recommendation logic built with LLMs
• Have set up CI/CD for data pipelines or product logic (automated testing, staged rollout, rollback), and have measured whether a change to a recommendation or model actually improved outcomes, not just shipped it
• A bias toward clarity and action
• Comfort working in a collaborative environment where data consumers are partners, not just stakeholders

• Exposure to distributed systems concepts (partitioning, consistency, fault tolerance)
• Hands-on experience with infrastructure as code (Terraform, Pulumi) in a cloud environment
• Experience with Hex, Mixpanel, Tableau, or similar BI/analytics tools
• Familiarity with data contract or data mesh patterns
• Experience with event tracking or product analytics

The estimated base salary range for this position is $185k to $210k, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.