About the Role:

The goal of our analytics architecture is to allow teams across Afresh to read and write meaningful, consistent, and reliable metrics derived from our disparate data sources, and to use those metrics to track internal performance, power new reporting products, drive decision-making through experimentation, and alert on significant changes in performance.

In the next 6 months, we are releasing new products that build on top of our analytics architecture, namely a customer facing product for reporting insights that will help our customers drive down food waste. As we venture into building more customer facing products that leverage analytics insight, we’re looking to strengthen our analytics platform foundation.

The Data Science team sits under the larger Prediction, Optimization, and Planning (POP) team at Afresh. You will regularly interact with data engineers, applied scientists, data scientists, full stack engineers, and product managers in the course of your work.

As a staff data engineer on the Data Science team, you will own the ongoing development of our analytics platform. In this role, you will evolve our data warehouse schema, solidify our transform architecture, and establish data governance patterns to serve our internal and external analytics needs. Some of your responsibilities will include:

• Improve and extend our data analytics architecture to enable consistent analytic results and easy access across multiple analytics use cases
• Collaborate with engineers, product managers, and data scientists to understand their data needs, and then build extensible dimensiional models and semantic layer metrics that allow for consistent and reliable insights
• Evolve our existing data quality and data governance processes
• Mentor and up-skill other engineers

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

Skills and Experience:

• 6+ years of experience as an data engineer, analytics engineer, data warehouse engineer, or a similar role.
• Strong understanding of advanced concepts in SQL.
• Exceptional communication and leadership skills, with a proven ability to facilitating cross-team and cross-functional collaboration and information sharing.
• 1+ years of experience working with SQL-driven transform libraries that support an ELT paradigm, like dbt or sqlmesh, at scale, including setting up CI/CD pipelines that ensure high quality transformations.
• Expert knowledge about the differences between OLTP and OLAP database design.
• Familiarity with the differences between data engineering concepts like Data Mesh, Data Lake, Data Warehouse, Data Fabric, and Data Lakehouse.
• Experience with setting up a semantic layer defined with code (LookML, Cube.dev, AtScale, dbt semantic layer).
• Technologies: SQL, Python, Airflow, dbt, Snowflake/Databricks/BigQuery, Spark.

Salary Band:

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