Sr. Product Manager

Job Description

Amazing Career Moments Happen Here

Transforming the insurance industry is ambitious, we know. That’s why at Applied, we’re building a team that shows up every day ready to learn, willing to try new things, and driven to deliver innovative software and services that make us indispensable to our customers – all within a culture built on values that make us indispensable to each other too. With 40+ years of experience in the Insurtech game, we’re not just redefining what’s achievable;

we’re creating a place where amazing career moments are made possible.

We are looking for a Senior Product Manager to own the product vision, strategy, and roadmap for our internal data lake. This is a high-impact, cross-functional role at the heart of our data organization - you will define how the company collects, governs, and derives value from its data assets, partnering closely with Data Engineering, Data Science, Analytics, and business stakeholders to build a best-in-class platform on Google Big Query.

This role demands both strategic thinking and operational depth - balancing long-term platform vision with near-term execution, navigating competing stakeholder priorities, and ensuring data quality, privacy, and governance are first-class concerns.

To keep us headed in the right direction, we’re looking for people who understand winning is a team sport, who value and seek to learn from our team’s diverse experiences and backgrounds, who welcome a challenge and take risks in the pursuit of creating better outcomes for our customers, and who can, at any given time, step back to laugh and have some fun.

• Define and own the multi-year data lake product strategy, translating business needs into a prioritized roadmap that balances platform reliability, new integrations, and insight enablement.
• Identify opportunities to consolidate data sources and reduce duplication; communicate vision and trade-offs clearly to executive, engineering, and business audiences.
• Serve as the primary product contact for data consumers across Finance, Marketing, Operations, Sales, and Engineering - running alignment sessions, managing expectations, and negotiating prioritization with senior stakeholders.
• Define and hold the platform team accountable to clear SLAs for data availability and quality.
• Own the intake process for onboarding new data sources - evaluating business value, technical complexity, and downstream impact including schema dependencies, data lineage, and fan‑out effects on pipelines and dashboards.
• Partner with Data Engineering to set ingestion standards and champion data cataloging so every dataset is findable and usable across the organization.
• Work with Data Science to prioritize feature store development, training dataset pipelines, and experiment tracking infrastructure based on model roadmap needs.
• Ensure teams have reliable, well-documented access to curated datasets, and co‑define success metrics that bridge raw data availability to actionable business intelligence.
• Build and own a comprehensive governance strategy on Big Query covering data classification, access control (RBAC/column-level security), retention policies, and audit logging.
• Define data ownership models and drive adoption of quality frameworks - profiling, lineage tracking, anomaly detection - while collaborating with Legal and Security.

Your experience may include:

• 7+ years of product management experience, with at least 2 years focused on data platform, data infrastructure, or analytics products.
• Demonstrated ability to manage complex, multi-stakeholder environments with competing priorities at senior levels.
• Strong understanding of modern data stack concepts: data lakes, warehouses, ELT pipelines, data catalogs, and governance frameworks.
• Hands‑on familiarity with Google Big Query
• Ability to engage credibly with data engineers and data scientists on technical architecture decisions; excellent communication skills for executive audiences.

You may have other skills or credentials, including:

• Experience implementing governance frameworks: data mesh, data contracts, or metadata…