Application Guide

How to Apply for Data Engineer

at Afresh Technologies

🏢 About Afresh Technologies

Afresh Technologies is an AI-driven platform uniquely focused on reducing millions of pounds of food waste annually in the fresh food supply chain. Working here means contributing directly to solving a critical environmental and social problem through technology, with a mission-driven culture that values both impact and innovation.

About This Role

This Data Engineer role involves building and maintaining robust data pipelines using PySpark, Python, and dbt to process billions of records from customer datasets, while also contributing to AI tooling adoption like LLM-assisted data cleaning. You'll directly impact the company's ability to onboard new customers faster and deliver data solutions that reduce food waste at scale.

💡 A Day in the Life

A typical day might involve optimizing PySpark jobs to process new customer datasets at billion-record scale, collaborating with product teams to design data solutions for new features, and experimenting with LLM-assisted tools to improve data cleaning workflows. You'll balance pipeline maintenance with building new integrations that help reduce food waste more effectively.

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Interview Prep

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🎯 Who Afresh Technologies Is Looking For

  • Has 2+ years of hands-on experience building ETLs/data workflows with Python, PySpark, and SQL, specifically with messy, incomplete datasets common in supply chain or retail data
  • Demonstrates practical experience or strong interest in Databricks, Snowflake, and dbt platforms, showing they can work within modern data stack environments
  • Shows evidence of identifying automation opportunities to simplify workflows, with examples of tooling they've built or improved to reduce manual effort
  • Exhibits curiosity about AI applications in data engineering, particularly LLM-assisted data cleaning, semantic validation, or anomaly detection

📝 Tips for Applying to Afresh Technologies

1

Highlight specific experience with PySpark and dbt in your resume, quantifying the scale of data you've processed (e.g., 'processed X billion records using PySpark')

2

Include a project example where you transformed messy, inconsistent datasets into structured data, emphasizing the business impact

3

Mention any experience with supply chain, retail, or sustainability data, as this aligns with Afresh's food waste reduction mission

4

Demonstrate your automation mindset by describing tools you've built to simplify data workflows or improve repeatability

5

Show awareness of Afresh's mission by connecting your data engineering experience to social/environmental impact in your application materials

✉️ What to Emphasize in Your Cover Letter

['Your experience with PySpark and dbt for processing large-scale customer datasets, with specific metrics on data volume and complexity handled', "Examples of improving data integration processes to make them faster and more repeatable, aligning with Afresh's need to onboard new customers efficiently", 'Interest or experience with AI/ML applications in data engineering, particularly around data cleaning, validation, or anomaly detection', "Why you're drawn to Afresh's specific mission of reducing food waste and how your skills contribute to this goal"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Afresh's specific food waste reduction metrics and case studies with grocery retailers to understand their impact and customer base
  • → The company's technology blog or engineering posts to learn about their current data stack and technical challenges
  • → Recent news about Afresh's AI initiatives or partnerships to understand their direction in AI tooling adoption
  • → The fresh food supply chain industry challenges to appreciate the context of the data problems you'd be solving

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Detailed technical discussion of PySpark optimization techniques for processing billions of records, including partitioning, caching, and performance tuning
2 Scenario-based questions about handling messy, incomplete fresh food supply chain datasets and transforming them into reliable data products
3 Your approach to designing repeatable customer data integration processes and tools to reduce manual effort
4 Experience with or ideas about applying LLMs or other AI tools to data cleaning, validation, or anomaly detection workflows
5 How you've collaborated with product, engineering, and go-to-market teams to deliver data solutions that meet business needs
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⚠️ Common Mistakes to Avoid

  • Only listing generic ETL experience without specific examples using PySpark, dbt, or handling billion-record scale datasets
  • Focusing solely on technical skills without showing interest in Afresh's mission or the food waste problem they're solving
  • Presenting as purely an executor without demonstrating automation mindset or examples of improving processes through tooling

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Afresh Technologies!

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