Senior Staff Data Engineer
A
AfreshFood Tech / AI
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, WIFull-TimeStaff
Salary191,000 - 287,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSQLETLSnowflakeAirflowData engineeringdbtDatabricksPySpark
Requirements
- Extensive experience (typically 8+ years) building data engineering systems, with a track record of operating at a staff or principal level.
- Deep technical expertise across Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
- A history of shipping high-quality data integrations or ETL systems at scale.
- Deep understanding of what makes data pipelines reliable.
- Proven ability to own ambiguous, end-to-end problems and set technical direction in a fast-moving environment.
- Genuine enthusiasm for AI-augmented engineering and experience with AI coding tools or agentic workflows.
- Comfort working with messy, real-world data from enterprise customers.
- Strong collaboration and influence skills across engineering, product, and customer-facing teams.
Responsibilities
- Architect and build core data systems and pipelines that power Afresh products, owning reliability and quality from raw data through to production.
- Take on our most ambiguous, high-leverage data problems and drive them to a shipped solution.
- Set technical direction: define the architecture, patterns, and abstractions that make customer integrations and product dataflows faster, cleaner, and more repeatable over time.
- Drive data quality and pipeline reliability — invest in better alerting, self-healing patterns, and resilience to messy or incomplete real-world customer data.
- Champion AI-forward engineering: evaluate and adopt AI tools and agentic workflows that accelerate development.
- Raise the bar technically — review code and architecture decisions, pair with engineers on hard problems, and mentor across the team.
- Collaborate deeply with Product, ML, Solutions Engineering, and customer-facing teams.
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