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

Minerva's data is not just infrastructure beneath our product; it is also one of our products. We are looking for a Data Engineer who can take ownership of complex consumer-data domains, develop a deep understanding of how their datasets relate and turn messy raw signals into trusted attributes and production data products. This role & Minerva are quite unique in the sense that GTM can immediately start selling your work output and generate enterprise-grade revenue in a matter of weeks.

You will spend most of your time at the transformation and derivation layer. You might become Minerva's internal expert on an identity graph, property and professional data, or a new source of consumer intent: learning the domain deeply enough to identify what is useful, what is misleading and what we should build next. Your job is not simply to make data available for someone downstream. You will use it to solve ambiguous problems and move an important part of our data product forward.

This is an end-to-end role. You will investigate novel datasets, source data when the answer is not already available, design domain models and derived attributes, and productionize your work so it can be consumed reliably by Minerva's applications, agents, APIs and ML models. Our data platform engineers build the infra that makes this work scalable; you must be comfortable operating within that infra and building your own ingestion and transformation pipelines without creating a bottleneck for the platform team.

The best fit can come from several backgrounds: a product-oriented data engineer, an applied data scientist who has data engineering skills, or a software engineer who has spent years solving difficult data problems. The common thread is first-principles reasoning, strong engineering fundamentals and a desire to own the answer from raw data through production. We test rigorously for data problem solving skills in our interview process.

WHAT YOU'LL DO

Own one or more complex consumer-data domains end-to-end, becoming the person responsible for both understanding the data and advancing the products built from it.

Investigate large, messy and unfamiliar datasets. Establish their grain, keys, relationships, coverage, failure modes and fitness for different product use cases.

Design and build durable domain models, derived attributes and entity relationships that can power Minerva's applications, AI agents, APIs, customer deliveries and predictive models.

Build and operate the ingestion and transformation pipelines required to bring your work into production, including validation, observability, backfills and recovery. Productionization is key.

Go on data quests: identify and evaluate new sources, determine how they can improve our consumer graph and find clever ways to extract signal from imperfect inputs. We often have budget for purchasing new data when there is clear ROI.

Make data outputs trustworthy enough to be consumed autonomously. Define quality checks, provenance and guardrails that distinguish reliable signal from convenient but misleading data.