Data Governance Lead

dv01 is lifting the curtain on the largest financial market in the world: structured finance. The $16+ trillion market is the backbone of everyday activities that empower financial freedom, from consolidating credit card debt and refinancing student loans, to buying a home and starting a small business.

dv01’s data analytics platform brings unparalleled transparency into investment performance and risk for lenders and Wall Street investors in structured products. As a data-first company, we wrangle critical loan data and build modern analytical tools that enable strategic decision-making for responsible lending. In a nutshell, we're helping prevent a repeat of the 2008 global financial crisis by offering the data and tools required to make smarter, data-driven decisions resulting in a safer world for all of us.

More than 400 of the largest financial institutions use dv01 for our coverage of over 75 million loans spanning mortgages, personal loans, auto, buy-now-pay-later programs, small business, and student loans. dv01 continues to expand coverage of new markets, adding loans monthly, and developing new technologies for the structured products universe.

The Problem and Opportunity

Our governance practices exist today, but they grew up alongside the product and are largely home-grown: definitions live in people's heads, ownership is informal, and data quality gets managed reactively, one escalation at a time. The Data Governance Lead will replace that with something deliberate. The priorities are named ownership and stewardship across our data domains, a correct and maintained data dictionary, a data quality framework with real metrics behind it, and disciplined compliance with the terms under which we receive client and third-party data. Cataloging, lineage, classification, and access controls follow from there.

We are building AI-powered products on top of that data, and that is what makes this role urgent. An AI product is only as trustworthy as the definitions, ownership, and quality controls underneath it. A model cannot reason correctly about a field nobody has defined, from a source nobody owns, at a quality level nobody measures. Governance is the constraint on how confidently and how quickly we can scale our AI and data products, and this role exists to remove it.

This is a builder's role and a relentless one. The pipeline build sits with Data Engineering and dataset definition with our Data Product Manager; you will work with them, with our teams of Data Analysts, and with Data Operations, Product, Commercial, and Legal to define what good looks like, get it instrumented, and hold the organization to it. It starts as an individual contributor role, with a team to be built as the function earns it.

You will:

• Scale What We Have Already Built for AI: Our data already powers AI products in production. The hard problem is doing that across every dataset, every client, and every new product without ever having to guess whether an answer is right. Our Data Product Manager defines what the data is; you make sure it is owned, classified, measured, and trusted at the scale AI demands. Nobody in this market has solved this yet, and we intend to be the ones who do.
• Own the Data Quality Framework and Its Metrics: Define what quality means across our loan-level and deal-level data, set the thresholds, and partner with our Data Product Manager, Data Engineering, and Data Analysts to get monitoring instrumented. Then report on it relentlessly and drive a sustained, measurable drop in failures.
• Establish Ownership and Stewardship: Put named owners and stewards on every data domain, document their decisions, and make sure those decisions stay current instead of going stale the week after they are made.
• Own the Data Dictionary and Business Definitions: Make our definitions correct, complete, and consistent everywhere they appear, from the warehouse to the product to what clients see. Chase down the ambiguities nobody else has time to chase.
• Own Data Rights in Practice: Know exactly what we are permitted to do with every dataset we receive from clients and third parties, and make those terms enforceable in the platform rather than buried in a contract nobody reads.
• Run It Like a Program: Track commitments, escalate what is stuck, and close things out. You will not have a team at the start, so persistence, credibility, and clear direction do the work.

You are:

• Accomplished as a data governance professional, bringing 7+ years of expertise in data governance, data quality, or data management.
• Proven at building programs from the ground up. What exists here today is home-grown at best, so we need someone who has stood a program up from nothing, not someone who has only administered one that was already running.
• Meticulous and exceptionally organized. You are the person who notices a definition is wrong, tracks down who owns it, and does not let it go until it is fixed.
• Fluent in technical conversations without needing to be an engineer. Able to review SQL, interpret data models, and partner effectively with engineering teams to guide the right technical architecture.
• Experienced in hands-on governance and data quality using purpose-built tooling rather than spreadsheets and home-grown trackers. We use BigQuery, dbt, and Unity Catalog today; experience with platforms like Collibra or Alation counts equally. What matters is that you have operated a real stack.
• Strategic about the intersection of AI and data governance. You have a proven track record of designing governance frameworks (covering quality, lineage, metadata, and compliance) that ensure AI models scale on trustworthy data.
• Knowledgeable across core governance domains (ownership, stewardship, quality, lineage, metadata, classification, access, master/reference data) and key regulatory frameworks (GDPR, CCPA, EU AI Act / ISO 42001).

Nice to haves:

• Experience managing data intended for external customer consumption
• Experience at a startup or startup-like environment
• Experience with structured finance and lending data