About the RoleAs a Staff Data Engineer, on the Distribution Center (DC) Solutions team, you'll play a technical lead role in building and scaling the data integrations needed to support our suite of DC products. You will design and implement ETLs that reliably process large volumes of customer-provided data and build tools/improve the platform to make customer integrations faster, more accurate, and more scalable. Your work will have a direct and visible impact on our ability to onboard customers more easily and quickly and power our machine learning grocery solution.
What You'll Do- Engineering & Data Pipelines: Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets while developing tools and frameworks to streamline data integrations and improve scalability.
- Technical Leadership & Strategy: Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
- Cross-functional Collaboration: Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.
What Makes You a Great FitWe encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification.
- Significant experience designing and maintaining ETLs that process large-scale datasets.
- Proficiency with Python, PySpark, SQL, and experience working on platforms/tools like Databricks, Snowflake, or DBT.
- 2+ years experience in a technical lead role (e.g. Tech Lead or Engineering Manager), with a willingness to mentor and help others grow.
- Strong problem-solving skills and the ability to work with ambiguous or incomplete requirements to deliver concrete, impactful solutions.
- A focus on practical outcomes-you're skilled at balancing technical rigor with the need to get things done.
- Experience working directly with complex, unclean datasets and finding innovative ways to process and analyze them.
- A knack for identifying areas where tooling or automation can simplify workflows and reduce manual effort.
- Excellent communication skills-you're able to explain your ideas clearly to both technical and non-technical audiences.
We're looking for someone who thrives on tackling complex data problems and takes pride in building systems that work seamlessly at scale. If that sounds like you, we'd love to hear from you!
This position is not eligible for immigration sponsorshipSalary Range in CAD: $148K - $242KWhy You'll Love Working at AfreshAt Afresh, our mission to eliminate food waste starts with investing in our people. We provide a comprehensive support system designed to help you do your best work while maintaining a healthy, balanced life.
- Comprehensive Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh. We also provide dedicated mental health support and counseling services.
- Invested in Your Future: Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
- Flexible & Modern Workspace: Whether you work from home or a local office, we support your setup with a home office stipend and "Coworking Wallets" for flexible workspace access.
- Growth-Obsessed Culture: We believe in continuous learning. Every employee receives an annual professional development budget to master new skills and grow their career at Afresh.
- Holistic Monthly Stipends: Beyond your paycheck, we provide monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications to ensure you have what you need to thrive.
- Time to Recharge: Flexible paid time off to take the time you need to recharge.
*Full-time U.S. employees are eligible for these benefits
Here at Afresh, 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, WI
