Senior Product Manager, Data Platform

Job description:

We are looking for a Senior Product Manager to lead our consumer data platform. We have a modern cloud lakehouse in place and are now building the consumer data foundation on top of it: a canonical data model, a consumer data warehouse, and one trusted source of data for analytics, data science, risk and finance teams.

You'll own the roadmap from the start. You'll decide which use cases to deliver first, define how core consumer entities are modeled, and make sure each new use case builds on shared data instead of one-off pipelines. You'll work closely with data engineering, data architecture, analytics and risk teams.

This is not a dashboard-building role. You'll own the data, definitions and metrics that dashboards, models and reports are built on.

What you'll do in your first 12 months:

• Build a prioritized backlog of consumer data use cases with analytics, data science, risk and finance, and decide which 3-5 to deliver first.
• Define canonical consumer entities, such as customer, account, transaction, fee, merchant, country and currency, so they mean the same thing across products.
• Decide where products share a common model and where they need their own structures, and document the reasoning.
• Define single, governed definitions for core consumer KPIs such as active customers, revenue per user, lifetime value and churn.
• Write requirements engineers can build from: source-to-target mappings, grain, keys, history rules and data-quality checks.
• Track adoption: which teams have moved to the new models, how much they're being used, and how many metric discrepancies have been resolved.

Required qualifications:

• Bachelor's degree in Computer Science or a related technical field, or an MBA, or equivalent practical experience.
• 6+ years of product management experience, including 2+ years in a data product or risk data role.
• Experience starting with many competing data requests, choosing the few to build first, defining an MVP, and explaining the trade-offs to stakeholders.
• Experience working with engineering to deliver a data lake/lakehouse, data warehouse or shared data model used by multiple teams.
• Experience bringing together inconsistent data from multiple source systems into common entities, keys and definitions.
• Hands-on SQL: you can join across tables, profile data for nulls and duplicates, reconcile counts between systems, and check whether data supports a use case without waiting for an analyst.

Preferred qualifications:

• Experience with consumer payments, digital wallets or fintech.
• Experience with payments risk data such as fraud, account takeover, KYC/AML or transaction monitoring signals.
• Experience with modern cloud data platforms and tools, such as Snowflake, Databricks, dbt or major cloud providers' data services.

Skills

Data Lake, Data Model, Data Warehouse, Lakehouse, Product, SQL