Data Platform Engineer, Ingestion

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.

Weve 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.

Every new data source starts as someone elses problem: an API with rate limits a vendor file that arrives late and gets corrected twice a schema that changes without warning. Today adding a new vendor feed still takes hand-built work.

You make new sources dependable and make the next one cheap. You build the ingestion pipelines for our customer and vendor data automate onboarding from configuration to first delivery and turn what you build into modules other Minerva teams can use without running platform infrastructure themselves.

This is a mid-level individual contributor seat on the data platform team reporting to our Head of Data Platform. Our data product team decides which sources matter and what good data looks like. You own how it arrives and how it stays dependable.

• Build pipelines for messy sources. APIs files database extracts and approved scraping sources at high volume with inconsistent formats and schemas that change.

• Make reruns safe. Incremental loads backfills and retries that never duplicate or corrupt data. Bad records get quarantined not loaded. Throughput and processing cost stay under control.

• Automate onboarding. The path from source configuration and access checks through provisioning validation deployment and first delivery runs without hand-holding.

• Build modules other teams use. Reusable connectors and storage components with tests examples and clear interfaces so a team building an API integration or a scraper gets managed ingestion storage and monitoring without building them.

• Keep the origin of every value. Carry source metadata through the pipeline (which vendor which delivery when) so we can always say where a value came from. Enforce privacy controls so restricted data cant come back through a new load or a replay.

• Own your integrations in production. Connect each one to shared monitoring write its recovery runbook and fix source-specific problems when they break.

• Orchestration: Dagster with Airflow still running the workloads that havent moved.

• Transformation: dbt and SQLMesh.

• Storage: Snowflake plus a lakehouse on Spark Iceberg Trino and AWS Glue.

• Code: Python on AWS.

• Typically three or more years building and supporting production data pipelines or equivalent ownership.

• Youve brought in data from many vendors and source types including messy data at scale and kept it running after launch.

• Practical judgment about incremental processing backfills failures and cost. You can show the data stayed correct as volume grew.

• Youve automated repeated work or built components other engineers actually used.

• Strong Python and SQL.

• You pin down what a source owner actually needs and you write down what you will and wont support.

• Youre in New York City or willing to relocate.

• Connector frameworks whether off the shelf or your own.

• Onboarding automation or self-service data tooling.

• Consumer identity or contact data and the privacy rules that come with it.

• Dagster Iceberg or Snowflake in production.

• A data engineer at a data vendor or identity company who has onboarded source after source and wants to build the framework instead of the next one-off.

• An engineer at a connector integration or reverse-ETL company who has built connectors other people depend on.

• A data engineer at a fintech marketplace or adtech company who became the person every new integration ran through.

• Or you dont fit any of these neatly but youve taken data you didnt control and made it dependable. Apply.

• First 90 days: a new source is live through automated onboarding you built and another engineer has onboarded a different source with the same modules. Youve measured the setup effort and tested recovery.

• First 6 months: teams outside data platform bring in new sources through your modules and every feed you own has alerting and a recovery runbook a second engineer has used.

• The numbers you own: time to onboard a new source and how often a feed needs hands-on recovery.

$170000$236000 base salary depending on level. Meaningful equity. Medical dental and vision.

Skip the cover letter. Walk us through one pipeline you built for data you didnt control: the source what broke how you made reruns safe and how the next source got easier. A few paragraphs. Applications without it wont be reviewed.

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

Every full-time role includes equity; medical dental and vision coverage; unlimited PTO; a monthly fitness stipend; and an annual company offsite. Wed rather pay people well than buy perks nobody uses.

The salary range in this posting is our good-faith range for the role in New York City at the time of posting. Where an offer lands depends on the level and experience we see in the interview process.

Apply on this page. We dont take applications by email and we dont accept unsolicited submissions from recruiting agencies. If you need an accommodation at any point in the process write to

Youll need to be authorized to work in the United States. We consider visa sponsorship case by case so tell us on the application if youll need it.

Minerva is an equal opportunity employer. We make hiring decisions without regard to race color religion creed sex pregnancy sexual orientation gender identity or expression national origin citizenship status age disability marital status veteran status or any other characteristic protected by federal New York State or New York City law.

Our hiring runs through Ashby and @ email addresses. We will never ask you for money bank details or other financial information during the process. If a message claiming to be from Minerva looks wrong forward it to

Required Experience:

IC