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Afresh is an AI platform for grocery retail that helps major chains like Albertsons, Meijer, and Wakefern optimize operations and reduce food waste. The company has achieved 70% revenue growth in 2025 and now operates across 6 enterprise solutions serving over 10% of the U.S. grocery market, preventing over 200 million pounds of food waste annually.
As Staff Data Engineer on the Data Science and Analytics team, you will be the technical leader of Afresh's analytics platform—the foundation for all data products ranging from standardized reports to AI-driven insight engines and agentic analytics tools. You will own the development, evolution, and optimization of the analytics infrastructure that serves both internal teams and customer-facing analytics needs.
Key responsibilities include:
- Improving, extending, and optimizing the data analytics architecture to provide reliable and accessible data for the suite of data products
- Collaborating with stakeholders across the company (data engineers, product engineers, product managers, data scientists, operations specialists) to understand data needs and build extensible dimensional models, semantic layer metrics, and agentic context and tooling
- Evolving existing data quality and data governance processes
- Mentoring and up-skilling other engineers
This is a high-impact role with ownership of highly visible projects and significant scope for growth. You will work on billion+ row datasets and help define how the world eats through reliable, well-modeled data and clearly-defined metrics.
REQUIREMENTS:
- 6+ years of experience as a data engineer, analytics engineer, or similar role
- Exceptional communication and leadership skills with proven ability to facilitate cross-team and cross-functional collaboration
- Strong drive to learn new skills and dive into unfamiliar domains
- Strong sense of ownership and agency—taking initiative to spot and solve problems independently
- Fluency in advanced SQL, dimensional modeling, and data warehouse design
- Experience with Cloud Data Warehouse platforms (Databricks, Snowflake, BigQuery, or similar)
- 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
