How to apply for Staff Data Engineer, Analytics

About Afresh Technologies

Afresh builds AI software for the fresh food supply chain. Its platform is used by grocers like Albertsons, Meijer, and Wakefern, and it prevented over 200 million pounds of food waste last year. The company reported 70% revenue growth in 2025 and its solutions are live in over 10% of the U.S. grocery market.

About the role

This is a Staff Data Engineer, Analytics role on the Data Science and Analytics team. You will be the technical leader of the analytics platform that powers data products including standardized reports, AI-driven insight engines, and agentic analytics tools. Your work supports customer-facing products that help grocers reduce food waste.

A typical day

You might review data model changes with data scientists, then work on platform reliability or performance issues that affect customer-facing reports and insight tools. You may also spend time planning how new agentic analytics features will consume the modeled data. The exact daily rhythm is not described in the job post, so ask about team ceremonies and on-call expectations in interviews.

Who Afresh Technologies is looking for

• Has experience as a technical lead or staff-level data engineer building and owning analytics platforms.
• Can design and maintain well-modeled data foundations that support reporting, AI-driven insights, and agentic analytics tools.
• Has worked with high-volatility or supply-chain data, ideally in retail, grocery, or fresh food environments.
• Is comfortable building data products that serve external customers, not just internal dashboards.

Tips for this application

• In your resume, highlight any analytics platform you have owned end-to-end, including data modeling, reliability, and how downstream products used it.
• Name specific data products you have shipped (reports, insight engines, agentic tools) and the scale they operated at.
• If you have worked with grocery, retail, or supply-chain data, make that explicit and describe the volatility or complexity of that data.
• Mention any experience with AI-driven or agentic analytics, since Afresh lists these as part of its data product suite.
• Address the remote nature of the role by showing you can lead technical work across distributed teams.

What to cover in your cover letter

['Your experience as a technical leader of an analytics platform, including how you made modeling and architecture decisions.', 'How you have built data foundations that support both standardized reporting and AI-driven or agentic analytics products.', 'Any direct exposure to fresh food, grocery, or high-volatility supply-chain data and why that context matters.', "Why reducing food waste through better analytics is a problem you want to work on, tied to Afresh's scale (200 million pounds prevented, 10% of U.S. grocery market)."]

Research before applying

• Read Afresh's product pages for its 6 enterprise-grade solutions and note which ones depend on analytics.
• Look up how Albertsons, Meijer, and Wakefern use Afresh, and what fresh food data challenges those grocers face.
• Understand what 'agentic analytics tools' means in the context of grocery and how they differ from standard reports.
• Check Afresh's engineering blog or public talks for details on their data stack and analytics platform architecture.

Likely interview topics

Based on the job description, expect questions about:

• How you would design a data model for high-volatility fresh food data that feeds both reports and AI-driven insight engines.
• A time you led the technical direction of an analytics platform and how you made trade-offs between reliability, speed, and flexibility.
• How you would support agentic analytics tools on top of a well-modeled data platform.
• How you have worked with data science teams to turn models or insights into customer-facing data products.
• Your approach to data quality and reliability when the underlying supply-chain data changes frequently.

Common mistakes to avoid

• Describing only internal dashboards or BI work when the role is about customer-facing data products at enterprise scale.
• Ignoring the fresh food or grocery context and treating the data as generic e-commerce or SaaS data.
• Applying as an individual contributor without showing evidence of technical leadership on a platform.