We're looking for a true data enthusiast: someone passionate about translating business needs into data solutions, building strong relationships, shaping methodologies and processes, and owning complex data initiatives from end to end.
This role will report to the VP of Product Management, Data.
What Youll Do
Data Product Ownership
Own the vision, roadmap, and delivery of critical datasets that underpin platform functionality, decisioning systems, and customer-facing features.
Lead modularization of data structures to enable scalability, easier ownership distribution, and data quality improvements.
Introduce metrics for quality (completeness, coverage, freshness, accuracy) and define validation, logging, and governance frameworks.
Cross-Functional Leadership
Serve as the product voice for data across R&D, aligning platform engineers, analysts, scientists, and business teams around a common strategy.
Bridge between domain-specific product teams and platform data infrastructure, translating business needs into scalable, reusable data assets.
Define data SLAs, fallback mechanisms, enrichment strategies, and quality assurance mechanisms in partnership with data consumers.
Strategic Impact
Prioritize and plan data initiatives that support new verticals, geographies, product features, and business models.
Spearhead productization of internal datasets into revenue-generating assets for both direct sales and platform distribution.
Deeply understand the customers evolving needs and enable the data to capture them accurately and scalably.
Execution & Optimization
Write clear product specs that include technical definitions, mapping rules, edge case 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 anomalies, misclassifications, and inconsistencies, and lead efforts to resolve them in systematic and productized ways.
Design workflows for historical tracking and versioning of datasets.
This role will report to the VP of Product Management, Data.
What Youll Do
Data Product Ownership
Own the vision, roadmap, and delivery of critical datasets that underpin platform functionality, decisioning systems, and customer-facing features.
Lead modularization of data structures to enable scalability, easier ownership distribution, and data quality improvements.
Introduce metrics for quality (completeness, coverage, freshness, accuracy) and define validation, logging, and governance frameworks.
Cross-Functional Leadership
Serve as the product voice for data across R&D, aligning platform engineers, analysts, scientists, and business teams around a common strategy.
Bridge between domain-specific product teams and platform data infrastructure, translating business needs into scalable, reusable data assets.
Define data SLAs, fallback mechanisms, enrichment strategies, and quality assurance mechanisms in partnership with data consumers.
Strategic Impact
Prioritize and plan data initiatives that support new verticals, geographies, product features, and business models.
Spearhead productization of internal datasets into revenue-generating assets for both direct sales and platform distribution.
Deeply understand the customers evolving needs and enable the data to capture them accurately and scalably.
Execution & Optimization
Write clear product specs that include technical definitions, mapping rules, edge case 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 anomalies, misclassifications, and inconsistencies, and lead efforts to resolve them in systematic and productized ways.
Design workflows for historical tracking and versioning of datasets.
Requirements:
Experience
4+ years of experience in data product management, or as a technical product manager or solutions architect in a big data environment.
Proven track record in leading cross-functional data initiatives involving data engineering, data science, and analytics teams.
Experience delivering data products for both revenue-generating direct sales use cases and scalable platform integration.
Hands-on experience with large-scale data collection, classification, curation, and delivery systems, especially in companies that build or sell data products.
Experience driving data modeling and transformation projects with complex entity relationships and high-quality standards.
Practical exposure to AI/ML productization: partnering with data scientists to define training data, evaluate model performance, and integrate AI outputs into production systems.
Skills
Strong analytical and systems-thinking mindset with deep understanding of how data flows and scales.
Ability to define validation frameworks, QA rules, and performance metrics for structured and semi-structured data.
Excellent communicator who can translate between technical and business stakeholders and align diverse interests into unified outcomes.
Collaborative leadership style with a bias for action and ability to resolve ambiguity through structure and logic.
Technical Fluency
Comfortable querying large datasets and working alongside large scale data pipelines.
Familiarity with Python, Spark, Airflow, and cloud data environments (e.g., Databricks, S3, Snowflake) is a strong plus.
Understanding of AI/ML lifecycle, including model inputs, inference pipelines, and continuous data feedback loops.
Experience
4+ years of experience in data product management, or as a technical product manager or solutions architect in a big data environment.
Proven track record in leading cross-functional data initiatives involving data engineering, data science, and analytics teams.
Experience delivering data products for both revenue-generating direct sales use cases and scalable platform integration.
Hands-on experience with large-scale data collection, classification, curation, and delivery systems, especially in companies that build or sell data products.
Experience driving data modeling and transformation projects with complex entity relationships and high-quality standards.
Practical exposure to AI/ML productization: partnering with data scientists to define training data, evaluate model performance, and integrate AI outputs into production systems.
Skills
Strong analytical and systems-thinking mindset with deep understanding of how data flows and scales.
Ability to define validation frameworks, QA rules, and performance metrics for structured and semi-structured data.
Excellent communicator who can translate between technical and business stakeholders and align diverse interests into unified outcomes.
Collaborative leadership style with a bias for action and ability to resolve ambiguity through structure and logic.
Technical Fluency
Comfortable querying large datasets and working alongside large scale data pipelines.
Familiarity with Python, Spark, Airflow, and cloud data environments (e.g., Databricks, S3, Snowflake) is a strong plus.
Understanding of AI/ML lifecycle, including model inputs, inference pipelines, and continuous data feedback loops.
This position is open to all candidates.
