Staff Data Engineer, Analytics

At Afresh, we’re building data products to define the future of retail grocery analytics. The nerve center of those products is our analytics platform, and as a Staff Data Engineer on the Data Science and Analytics team, you’ll be the technical leader of that platform. 

Our growing suite of data products range from standardized reports to AI-driven insight engines and agentic analytics tools. They let our customers understand what’s happening in their stores and empower them to reduce food waste. All of these products require a reliable platform of well-modeled data, clearly-defined metrics, and extensive metadata and context. 

This platform is also used by teams across the company to ensure customer success and improve our core products. The Data Science and Analytics team collaborates closely with every team in the company, helping them build products and make decisions with data. You will regularly interact with data engineers, product  engineers, product managers, data scientists, and operations specialists.

What You'll Do:

As a Staff Data Engineer on the Data Science team, you will own the development of our analytics platform. You’ll evolve our data models, solidify our pipelines, and establish data governance patterns to serve our internal- and customer-facing analytics needs. Some of your responsibilities will include:

• Improving, extending, and optimizing our data analytics architecture to provide reliable and accessible data for our suite of data products;
• Collaborating with stakeholders across the company to understand their data needs, and then build extensible dimensional models, semantic layer metrics, and agentic context and tooling that will allow for consistent and reliable AI-assisted insights;
• Evolving our existing data quality and data governance processes; and
• Mentoring and up-skilling other engineers

This is a high-impact role with ownership of highly visible  projects and a lot of room to grow in your scope.

• 6+ years of experience as a data engineer, analytics engineer, or similar role.
• Exceptional communication and leadership skills, with a proven ability to facilitate cross-team and cross-functional collaboration and information-sharing.
• A drive to learn new skills and dive into unfamiliar domains.
• A strong sense of ownership and agency — taking initiative to spot problems and solve them without waiting to be told. 
• Fluency in advanced concepts in SQL, dimensional modeling, and data warehouse design; experience with Cloud Data Warehouse platforms (Databricks, Snowflake, BigQuery, etc.)
• Proficiency in dbt 
• Experience with orchestration tools (Airflow/Astronomer, Dagster, or similar)
• Experience with modern semantic layers: LookML, Cube.dev, Databricks Metrics Views, dbt MetricFlow, etc.).
• Experience designing and optimizing pipelines of billion+ row datasets
• Experience working with and extending AI models to produce high quality code and reliable analytics. Experience working on  

Founded in 2017, Afresh is using AI to tackle the #1 solution to curb climate change: reducing food waste. By building AI specifically for the intricacies of grocery—from the fresh perimeter to the center store—we help grocers minimize waste and maximize sales.

Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals, including ICML, and our investors include Al Gore’s Just Climate, former Whole Foods Market CEO Walter Robb, and Eric Schmidt's Innovation Endeavors.

Grocery is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.

Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.