Senior Product Manager - Data Platform and Analytics

Pie's mission is to empower small businesses to thrive by making commercial insurance affordable and as easy as pie. We leverage technology to transform how small businesses buy and experience commercial insurance.   Like our small business customers, we are a diverse team of builders, dreamers, and entrepreneurs who are driven by core values and operating principles that guide every decision we make.

The Senior Product Manager is a skilled product practitioner with demonstrated ability to drive significant business impact while developing sophisticated strategic capabilities. They operate with high autonomy in complex business domains while maintaining appropriate engagement with leadership on strategic direction. They identify and execute on high-value opportunities, build effective cross-functional partnerships, and deliver measurable outcomes that advance Pie's strategic goals.

You will report to the Director of Product for Data and Post-Bind Journeys (financial systems, premium audit, and claims) and work closely with Data Engineering leadership, Data Science teams, and cross-functional partners to identify opportunities, prioritize initiatives, and drive end-to-end execution from conception to impact measurement. You'll manage backlogs across analytics and platform domains, diving into detail mode from an understanding perspective.

Mission For This Role

The Senior Product Manager for Data Platform & Analytics will drive critical initiatives that enhance Pie's data infrastructure and analytics capabilities, focusing on improving data quality and platform capabilities, accelerating analytics delivery, and expanding data self-service capabilities across the organization. This role will contribute to making data and analytics a competitive advantage for Pie through reliable infrastructure and accessible insights.

How you’ll do it 

Data Platform:

The Data Platform team builds the infrastructure that moves data from operational systems to our analytical environment This role requires deep technical understanding to make informed product decisions while working closely with Data Engineering teams who own technical implementation.. You'll provide product oversight and prioritization for ETL pipeline improvements using tools like Snowflake and Airflow, prioritizing reliability, observability, and efficiency. You'll manage the transition from legacy data sources to system-generated data, reducing manual intervention and increasing automation.Your technical fluency will enable you to make informed product decisions while partnering with engineering teams who own implementation and defining product requirements that enable scalable analytics. 

Analytics Enablement:

You'll focus on creating and prioritizing  self-service analytics capabilities that empower teams across Pie to make data-driven decisions independently. This includes developing data marts, improving data discoverability, and building tools that enable business users to access and analyze data efficiently. You'll work closely with business stakeholders to understand their analytical needs and translate them into scalable data products that reduce dependency on manual reporting and ad-hoc analysis.

Key Outcomes For First 12 Months:

• Strategic Roadmap Development: Develop a comprehensive data platform and analytics roadmap for your domain that balances technical excellence with tangible business impact, clearly connecting improved data capabilities to Pie's competitive advantage in the mid/high risk workers comp and commercial auto segments.
• Product Operating Model Integration: Successfully integrate your data platform domain within Pie's Product Operating Model, adapting ceremonies and workflows to accommodate the unique characteristics of data products while maintaining agile principles.
• Cross-functional Partnership Building: Build strong, collaborative relationships with technology and business leaders across Pie, establishing yourself as a trusted product partner on data platform strategy and analytics implementation through both technical expertise and business acumen.
• Data Reliability Improvement: Partner with engineering to identify and prioritize improvements that will allow for more durable data contracts between operational systems and data pipelines.
• Manual Process Reduction: Develop and execute a strategy for progressively reducing reliance on manual/spreadsheet-based data sources and reporting, with clear priorities based on business impact and technical feasibility.
• Measurement Framework Implementation: Create and implement a measurement framework for data quality, pipeline reliabil