Staff Data Engineer

About the role

ABOUT MINERVA

Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.

Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.

We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.

ABOUT THE ROLE

We run a multi-tenant transformation platform that unifies a portfolio of customer brands into a single golden data model across Shopify, HubSpot, Klaviyo, Google/Meta Ads and more.

You'll be the technical owner of this part of our broader data platform: the person who designs for the regime we're growing into rather than the one we're in. The defining question of the role is how to make the cost of the next tenant (and the next data source, the next entity in our ontology, the next golden model) flat instead of linear, while correctness, isolation and freshness hold as the system fans out.

Increasingly, that platform isn't operated only by people. In partnership with OpenAI, our Agentic Data Engineer (ADE) already standardizes bespoke client data into golden records, and downstream agents like our Agentic Data Scientist (ADS) build models on top of it. You own the substrate they stand on (the contracts, the golden model, the access layer) and the systems that expose our data to internal and external AI agents (MCP, vector search) as a first-class channel. We believe agentic access to our data is just as important as the traditional API and ETL paths, if not more, and this role builds for that.

This is a build-from-strength role. We expect data cleaning, modeling and warehouse fluency to be second nature so your thinking is free for architecture and large-scale initiatives, especially given how much leverage modern AI coding tools give a strong engineer.

WHAT YOU'LL DO

- Design the tenant-onboarding model so adding the Nth tenant is a config-and-metadata operation, not bespoke engineering, driving source routing, transformation, model membership and per-tenant view generation from a single source of truth rather than hand-assembly

- Own correctness guarantees across a wide fan-out of tenants and models: the class of problem where coverage gaps, contract drift and partial rollouts can hide unless the system makes them structurally impossible. Build the contracts, audits and CI gates that turn "is every tenant fully represented?" into something the build answers, not a person