Lead Solutions Engineer, Integrations
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
Technologies
SQL · Excel · data architecture · data integration · predictive analytics · ML model training · data mapping
About Afresh
AI platform for grocers that optimizes fresh-food ordering, inventory, merchandising and supply chain to cut shrink, lift sales and reduce food waste.
Private Late · 100–200 people
Job description
The full responsibilities and requirements are on the employer’s site.
Read the job description ↗Source and classification
Implementation & delivery · Evidence for this classification:
Role As a Lead Solutions Engineer at Afresh, you will work directly with our grocery retail partners and Afresh colleagues to deploy our purpose-built fresh food solution and bring tangible, lasting impact to our partners. Externally, you will build effective partnerships with customers to drive the success of end-to-end integrations. You will apply a skillset at the intersection of data architecture, operations, and predictive analytics to translate customers' business needs into technical design for integration to the Afresh platform. Internally, you'll collaborate cross-functionally and act as a connecting force between our integration technology and customers' operational reality. You have experience managing customer relationships and leading technical projects in fast-paced environments. You are excited to reduce fresh food waste in the grocery industry, and help the planet in
More from the job description
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 Lead Solutions Engineer at Afresh, you will work directly with our grocery retail partners and Afresh colleagues to deploy our purpose-built fresh food solution and bring tangible, lasting impact to our partners. Externally, you will build effective partnerships with customers to drive the success of end-to-end integrations. You will apply a skillset at the intersection of data architecture, operations, and predictive analytics to translate customers' business needs into technical des [... source excerpt omitted ...] nternally, you'll collaborate cross-functionally and act as a connecting force between our integration technology and customers' operational reality. You have experience managing customer relationships and leading technical projects in fast-paced environments. You are excited to reduce fresh food waste in the grocery industry, and help the planet in the process! What You'll Do A typical day might involve discussing data architecture with engineers, digging into customer data, and developing technical implementation strategies and specifications. Specifically, you will: Own and execute integration projects from customer discovery through go-live — creating detailed project pla [... source excerpt omitted ...] estones, risk mitigation, and success criteria, and coordinating internal and customer-facing stakeholders through each phase Lead technical discovery and design engagements with customers, including multi-day on-sites where you independently scope 10–15 data interfaces, negotiate delivery timelines and formats, and leave with documented data integration specifications that power ML model training and deployment Apply deep knowledge of Afresh's core data model and integration patterns to evaluate whether customer needs can be met through standard approaches; flag misalignments and propose adjustments to design for the customer's approach Analyze complex data relationships, ide
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