Job Opportunities API

Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers. By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise. Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U.S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone. If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us. 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 work