Senior Data Product Manager, Core Data

Were looking for a Senior Data Product Manager to drive the strategy, architecture, and execution of the data products that power our company. This role is at the core of our data innovation engine, serving multiple business units and platform products across the company. You will lead cross-functional efforts to define, experiment, deliver, and scale data assets that are foundational to the company products and its market differentiation.

This role offers a unique opportunity to lead high-scale, high-impact data products by collaborating with various R&D data teams, product, and business stakeholders to architect modular, trusted, large scale and intelligent data systems.
We're looking for a true data enthusiast: someone passionate about translating business needs into data solutions, experimenting with data, building strong relationships, shaping methodologies and processes for various data sets, and owning complex data initiatives from end to end.
This role will report to the Director of Product Management, Core CI.
What Youll Do
Data Product Ownership
Own the vision, roadmap, and execution of foundational datasets and delivered metrics that underpin platform functionality, decisioning systems and customer-facing features.
Define success criteria for data products, including coverage, accuracy, freshness, stability, explainability and cost-to-serve.
Lead the design and evolution of scalable, modular data structures and models to provide easier ownership distribution, large coverage and data quality improvements.
Introduce and maintain data quality frameworks including validation rules, monitoring, logging, alerting and governance mechanisms.
Design, evaluate and iterate on core metrics for specific marketing acquisitions channels, cross channels performance and market benchmarks
Experimentation, Prototyping and Validation
Prototype new metrics, datasets and methodologies using different frameworks and languages to create fast proof of concepts.
Experiment with new internal and external data sources and define a clear path for productization.
Define validation strategies including backtesting, triangulation against analytics benchmarks, stability analysis and anomaly detection.
Own and document methodology narratives, assumptions, limitations and appropriate use cases.
Cross-Functional Leadership
Serve as the product voice for data across R&D
Align data engineers, data scientists, analysts and product and business teams around shared data strategy.
Bridge between domain-specific product teams and core data platform
Define data SLAs, fallback strategies, enrichment logic and quality threshold with data consumers.
Partner with product marketing, sales and customer success to support launch and customer facing methodologies discussions.
Execution & Optimization
Write clear detailed product specs that include data definitions, entity mapping, edge cases handling and downstream consumption logic.
Work hands-on with Databricks, Spark, SQL, and Python to analyze datasets, test logic, or collaborate on experiments.
Identify data gaps, anomalies, misclassifications, and inconsistencies and resolve lead efforts to resolve them in systematic productized ways.
Design dataset versioning, historical tracking, backfills and controlled evolution over time.