- Location
- San Bruno, California · San Bruno, California, United States
- Department
- Software
- Experience
- 5+ years
- Source
- Greenhouse
Description
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
What We're Looking For
- 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, BigQuery, 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
Nice to Have
- 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.
