Staff Data Engineer

ABOUT MINERVA

Minerva builds AI for marketing leaders. Our platform lets marketers focus on telling the story of their brand while AI agents handle the operationally intensive work: data management analytics campaign generation measurement and reporting.

Everything is built on Minervas proprietary consumer graph: an identity and attribute layer covering 270M U.S. consumers across 2000 through-time attributes. On top of it sit two agentic systems built in partnership with OpenAI: an Agentic Data Engineer that unifies and standardizes a brands first-party data in hours and an Agentic Data Scientist that trains robust targeting models at scale. Together our data and platform improve the quality of a brands first-party data lift campaign performance and give marketing teams their time back.

We work with leading consumer brands across categories including the NBA Capital One Hard Rock Stadium Group / Miami Dolphins Wander and Trust & Will. 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 and Lazard alongside 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.

Youll be the technical owner of this part of our broader data platform: the person who designs for the regime were growing into rather than the one were 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 isnt operated only by 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 YOULL 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

• Own the org-identity and access layer (tenant provisioning source-to-tenant routing multi-tenant isolation and RBAC) so it holds at hundreds of tenants without per-tenant special cases

• Build the platform machinery that lets our data ontology grow (new entities attributes and behaviors) without breaking everything downstream. Own how the ontology is represented versioned and propagated: schema evolution contract migration and backward compatibility across hundreds of tenants and the models built on them

• Partner with the field as our FDEs and customers surface the new concepts a customer needs making adding them to the canonical model a safe repeatable operation rather than a risky one-off

• Build the systems that expose Minervas data product to internal and external AI agents (MCP servers vector search semantic and metrics layers) so agents can query reason over and act on our data safely. Treat agentic access as a first-class delivery channel alongside API batch ETL and CRM integrations

• Own the substrate our agentic workflows depend on: the golden-record contracts ADE produces and ADS consumes. Build the guardrails validation and observability that let agent-generated transformations be trusted in production at scale. You build the reliability layer under the agents

• Lead the evaluation and migration of our orchestration and transformation stack as throughput concurrency and latency demands outgrow what todays tooling is built for incrementally without halting the business running on top of it

• Architect the path to real-time: keeping the analytical warehouse in sync with the Postgres/Elasticsearch product backends and standing up the infrastructure for the CDP (streaming audiences identity resolution reverse-ETL to ad platforms and CRMs)

• Define how pipelines are built tested deployed and governed as the team and tenant count grow: isolation privacy/opt-out enforcement and data-quality SLAs as first-class platform properties

WHAT YOU HAVE

• 5 years in data/software engineering in a data-heavy context with real ownership of a production data platform at scale (think XX TB many sources many consumers)

• Expert Python and SQL fluent enough that ingestion and modeling are reflexes not projects

• Deep experience with transformation and orchestration tooling (SQLMesh dbt Airflow or equivalent) and a point of view on where they break and what comes next

• Strong grasp of analytical warehouses (Snowflake/Redshift/BigQuery) and how they interplay with transactional stores (Postgres/MySQL)

• A track record of multi-tenant design: isolation governance and secure access patterns as tenant count climbs

• Demonstrated judgment on the stability vs. speed of development tradeoff: youve kept a platform reliable for its users while still letting a team ship fast on top of it and you can articulate how you decided where each belonged

• Experience evolving a schema data model or ontology in production (versioning migration and backward compatibility) without breaking downstream consumers

• Interest in (or experience) exposing data to AI agents (MCP vector search semantic layers retrieval) and a conviction that agentic access is a first-class way to deliver a data product

• Comfort owning ambiguous cross-cutting initiatives end-to-end in a lean fast-changing environment. Willing to work onsite in NYC (relocation provided)

NICE TO HAVES

• Lakehouse or Spark experience and comfort handling XX TB datasets

• Built or operated a CDP identity graph or reverse-ETL/activation system

• Strong communication skills preferably has interfaced with enterprise customers before

• Data lineage and reactive-propagation systems; streaming (Kafka/Flink/Snowpipe)

• Backend or ML/AI-eng exposure; feature engineering and training infra are adjacent to this role

• Heavy and efficient use of AI coding tools (Claude Code Cursor etc.) as a force multiplier

• Built infrastructure for agentic or LLM workflows on top of data (OpenAI Agents SDK MCP servers vector DBs retrieval and eval harnesses)

• Prior early-stage startup experience

Counterpart role: you own the technical side of evolving the ontology and platform; our Forward Deployed Engineer sits with the customer expands the ontology from the field and feeds you the patterns worth productizing.

COMPENSATION

$200000-$250000 base salary commensurate with experience. Meaningful equity. Performance Bonus. Medical dental and vision.

Apply: Subject: Staff Data Engineer Candidate

Required Experience:

Staff IC