Data Product Manager, Methodology & Semantic Governance

Curinos empowers financial institutions to put customers at the center of every decision. Our AI-first platform transforms proprietary data, advanced analytics and deep financial services expertise into timely recommendations - delivered right where teams work. The result: confident decisions, stronger customer relationships, and lasting, profitable growth.

Curinos operates under a hybrid modality and has office locations in New York, Chicago, Boston, Toronto, and London. This role is open to hybrid/ remote candidates based in the United States.

This highly visible individual contributor role sits at the intersection of Product, Platform, Analytics, and Subject Matter Expertise.

You will establish and maintain the standards that govern how Curinos benchmarks and metrics are defined, calculated, approved, and evolved. In practice, benchmark methodology includes the rules, calculations, eligibility and sufficiency criteria, data sources, quality standards, contractual considerations, and governance processes that make consistent, comparable, and trusted.

You will also serve as the steward of the shared semantic layer that powers Curinos One. This semantic layer is the common foundation for business logic, canonical metric definitions, calculation rules, metadata, lineage, and analytical concepts. It lets teams build from the same definitions rather than recreating them product by product, whether the consumer is a BI dashboard, an internal analyst, or an AI-driven feature within Curinos One.

This role combines analytical rigor, strong process leadership, and hands-on data investigation. You will dig into datasets, calculations, and business logic while designing governance that teams want to use because it reduces ambiguity, improves quality, and helps them move faster.

You own the semantic layer and benchmark methodology as a data product, with a roadmap focused on trust, quality, and reusability rather than customer-facing features, and with real authority over what gets approved and how the underlying data asset is maintained over time.

• Define Benchmark Methodologies Own the standards, rules, calculations, eligibility criteria, data-use considerations, and documentation that make Curinos benchmarks consistent, explainable, and fit for client use
• Govern Methodology Decisions and Change Hold approval authority over benchmark methodology changes. Lead the review and approval process for benchmark methodology changes, working with Product, Data, Risk, Legal, and client-facing teams to evaluate trade-offs, weigh controls, and reach sound, well-supported decisions
• Build the Shared Semantic Foundation for Curinos One Steward the common metric definitions, business logic, metadata, lineage, and glossary, including field-level definitions and data grain specifications, so teams can reuse trusted analytical concepts across
• Steward the Semantic Layer as a Living Asset
• Act as the accountable owner of the semantic layer's ongoing health, including data quality, lineage accuracy, and definition drift over time. When a metric definition degrades, a data source changes, or lineage breaks, you are the one who catches it, diagnoses it, and drives the fix, not just the one who wrote the original standard

• Benchmark methodologies are consistent, scalable, explainable, and trusted across products
• Teams confidently reuse shared metrics and business logic rather than reinventing them
• Methodology decisions are transparent, well documented, and easy to understand
• Governance improves decision quality and delivery speed without creating unnecessary overhead
• Curinos One operates from a trusted, well-governed semantic foundation
• Stakeholders view you as the go-to expert for benchmark methodology, business logic, and metric governance
• A living methodology handbook and metric catalog exist, are version controlled, and are the reference point teams cite instead of asking you directly
• A documented change control process (intake, review, approval, communication) is in place and followed, with a clear audit trail for methodology changes
• The semantic layer's health (data quality, lineage completeness, definition currency) is actively monitored, not just documented once and left to decay
• When something breaks or drifts, a source change, a definition going stale, a quality issue, you catch it early through proactive monitoring rather than after the fact

Salary Range: $145,000- $155,000 USD (plus Bonus)