The opportunity
Enterprise Foundations is the engineering team responsible for the architectural and integration work that allows Instacart's Retailer Platform to scale across the world's largest grocers. Our work spans omnichannel integrations — bringing products like FoodStorm catering and…
What you'll do
Lead, coach, and grow a senior-skewed team of ~12 engineers across multiple: workstreams; set clear architectural and execution direction, develop talent, and foster a culture of ownership, accountability, and continuous improvement.
Set and maintain the unifying technical thesis for the team: multi-tenancy, data isolation, configuration-driven release management, and per-retailer extensibility — ensuring related workstreams converge on shared solutions rather than parallel implementations.
Drive consolidation of bespoke retailer-vertical services: catering order management, smart-cart backend logic, kiosk integrations, in-store mobile — onto unified Instacart platform rails, so each surface inherits Instacart's ranking, search, recommendations, and fulfillment.
Partner with ML and Data Engineering on long-term enterprise data platform: compliance for our ML systems, ensuring per-retailer model training has the data, signals, and isolation it needs.
Deliver self-service tooling: including sandbox environments, configuration interfaces, and retailer-facing extensibility — that lets retailers and internal teams ship without engineering bottlenecks.
Represent technical and product trade-offs across multiple engineering and: product organizations whose roadmaps and integration commitments evolve in real time; broker resourcing decisions, drive clarity in ambiguous scope, and ensure engineering investment stays aligned with committed work.
What they're looking for
- Direct domain experience in commerce platforms, omnichannel retail, OMS, POS,: catering systems, or connected-store / in-store technology.
- Track record of building multi-tenant platforms serving multiple internal or: external customers, with per-tenant configuration, isolation, and extensibility requirements.
- Experience partnering with ML and Data Engineering teams on data platform: compliance, model training infrastructure, or data isolation initiatives.
- Background scaling enterprise integrations and managing partner-facing: technical relationships across product, engineering, and customer-facing functions.
- Experience leading other engineering managers and growing two-layer organizations.
