About the role
Mill is a waste prevention technology company reimagining what it means to eliminate waste, starting with food. We build smart systems and infrastructure for homes, businesses, and municipalities that transform food scraps from landfill-bound waste into valuable resources, including chicken feed. Tens of thousands of Mill’s residential food recyclers are already helping households divert millions of pounds of food scraps every year, paving the way for our upcoming launch of Mill Commercial—the industry’s first end-to-end solution for managing, understanding, and preventing food waste in commercial environments (e.g. grocery, restaurants, food services). At Mill, we are passionate about building easy-to-use, beautifully designed technologies that keep food in the food system and out of landfills.
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.
What You'll Do 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
