Job Description

About the Role

As a Senior Staff Data Engineer at Afresh, you'll be one of our most senior individual contributors — setting technical direction for how we build, integrate, and scale the data systems that power Afresh's products. Afresh's customer base is growing fast, and the bar for what our data foundations need to do is rising with it.

You'll own some of our most complex and ambiguous data problems end to end: from raw data ingestion through transformation and delivery to downstream product and ML teams, across both new and existing customers. Beyond shipping, you'll define the architecture, abstractions, and tooling that make every future integration faster and more reliable — and you'll raise the engineering bar for everyone around you.

This is a hands-on, high-ownership role. You'll be deep in the code and the architecture while also influencing direction across teams, mentoring other engineers, and partnering closely with Product, ML, Solutions Engineering, and customer-facing teams.

What You'll Do

• 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 — without waiting for a detailed spec.
• 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, automate repetitive work, and push the team to the bleeding edge of modern data engineering.
• 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 to scope work, unblock dependencies, and make sure what we build meets real customer needs.

What Makes You a Great Fit

We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.

• 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, and a 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 with no established playbook.
• Genuine enthusiasm for AI-augmented engineering. You've experimented with AI coding tools, agentic workflows, or similar, and have a vision for how they can transform data engineering.
• Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering.
• Strong collaboration and influence skills — you're effective working across engineering, product, and customer-facing teams, and you can move others without formal authority.

Nice to Have

• Experience in grocery, retail, or supply chain data domains.
• Prior experience at a high-growth startup navigating rapid customer expansion.
• A history of acting as a technical leader who sets direction across multiple teams or projects.

Our Tech Stack

• Python, PySpark, dbt
• Databricks (Delta Lake, Unity Catalog)
• Astronomer (Airflow) for orchestration
• Claude, GitHub, Shortcut, Notion for development workflows

This position is not eligible for company sponsorship.

Salary Range in U.S.: $191,000- $287,000