Data Engineer, Data Products

The posting, as Minerva wrote it

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 d
[…]
ols.

• Comfortable building and deploying production data pipelines, not just analyzing data in notebooks or handing specifications to another engineering team.
• Able to balance analytical depth with engineering pragmatism: you care whether an attribute is conceptually valid and whether it can be produced reliably at scale; you know when a new idea won't provide any lift.
• Comfortable owning an ambiguous initiative end-to-end in a lean, fast-changing environment. You're more product-minded than people give you credit for.
• Willingness to work in our New York City office. We provide a relocation package.
• Eagerness to learn, grow and raise the bar with your coworkers.

PREFERRED

• Experience working with large, messy, multi-source datasets where the semantics were not obvious and documentation was incomplete.
• Experience with consumer data, identity resolution, entity graphs, property data, behavioral or intent data, or other complex third-party data domains.
• Experience with orchestration tools such as Dagster, Airflow or Prefect and transformation tools such as dbt or SQLMesh.
• Experience with analytical databases such as Snowflake, Redshift or BigQuery and familiarity with transactional databases such as Postgres or MySQL.
• Experience with lakehouse or distributed-processing systems such as Spark, Iceberg, Trino or AWS Glue.
• Familiarity with AWS or another major cloud platform.
• Exposure to ML eng/ops, applied ML or feature engineering. Deep modeling expertise is not required.
• Effective use of AI coding tools such as Claude Code, Cursor or OpenCode as a force multiplier.
• Prior experience at an early-stage startup.

You do not need to tick every box. If you are strong with data, we want to hear from you.

COMPENSATION

$170,000–$236,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision.

WHERE WE WORK

We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.