Senior Product Manager - Enterprise Data Products

Standard Insurance Company • Portland, OR

3d • Remote

About The Position

The Senior Product Manager in Enterprise Data owns the strategy, roadmap, prioritization, lifecycle, adoption, and business outcomes for a portfolio of enterprise data products. The role turns enterprise data into reliable products for internal business teams, analysts, operational systems, digital experiences, AI solutions, partners, and other consumers. Using a data-as-a-product approach, the position helps deliver analytical and transactional data products across the data lifecycle.

The Senior Product Manager works with customers and stakeholders to translate data needs for decisions, experiences, processes, and downstream products into defined products with measurable outcomes. Key partners include Data Engineering, Architecture, Data Governance, Analytics, AI, application teams, and business teams. Success means customers can find, understand, trust, access, and use data that measurably improves processes, experiences, and decisions.

The role leads complex products across systems, business areas, and delivery teams; makes product decisions that balance customer needs, enterprise priorities, risk, governance, and engineering constraints; and communicates outcomes and portfolio health to business and technology leaders. It also mentors Product Managers and Product Owners and helps mature Enterprise Data product-management practices.

Requirements

• High school diploma or GED required.
• At least 8 years of progressive experience in product management, data, analytics, technology, or a related business environment.
• Experience owning complex products through discovery, delivery, adoption, and lifecycle management.
• Experience building data, analytics, API, integration, digital, or enterprise technology products.
• Experience serving internal customers and supporting external customer experiences.
• Experience partnering with Data Engineering, Software Engineering, Architecture, Analytics, or Data Governance.
• Experience navigating complex enterprises, multiple stakeholders, and competing priorities.
• Strong discovery, strategy, roadmap, prioritization, and lifecycle-management skills.
• Ability to identify customer problems beyond stated requirements and distinguish reusable capabilities from one-time requests.
• Ability to translate a clear product vision into executable priorities, define outcomes, and measure product success.
• Strong customer discovery and stakeholder-management skills.
• Ability to make difficult tradeoffs using customer value, outcomes, risk, strategic alignment, and technical feasibility.
• Strong written, verbal, and visual communication skills.
• Strong understanding of data-as-a-product principles and modern enterprise data architectures.
• Understanding of ingestion, transformation, modeling, quality, storage, integration, and delivery.
• Understanding of analytical and transactional product patterns, including APIs, events, streaming, data sharing, and batch delivery.
• Understanding of metadata, lineage, business definitions, quality, privacy, security, and access controls.
• Ability to discuss architecture and engineering tradeoffs without personally designing or implementing solutions.
• Understanding of how analytics, machine learning, GenAI, and operational applications use enterprise data and how product design affects outcomes.
• Ability to identify shared data products serving analytics, AI, operational, and digital use cases and apply emerging Data and AI capabilities to customer needs and roadmap decisions.

Nice To Haves

• Bachelor’s degree.
• Experience working with a globally dispersed team.
• Familiarity with cloud data platforms and lakehouse architecture.
• Working knowledge of Azure Databricks, Delta Lake, Unity Catalog, SQL, APIs, and related technologies.

Responsibilities

• Own the portfolio vision, strategy, lifecycle, and forward-looking roadmap, explaining the direction and rationale.
• Translate customer problems, enterprise strategy, regulatory requirements, and technology opportunities into priorities.
• Define each product’s customers, problem, value proposition, and measurable outcomes.
• Prioritize investments and resolve competing needs using customer value, reuse, strategic alignment, risk, effort, and outcomes.
• Balance new development with enhancements, technical debt, quality, modernization, and lifecycle needs.
• Discover internal and external customer workflows, decisions, experiences, and data needs; engage business, operations, analytics, application, digital-product, and partner stakeholders.
• Translate customer problems into capabilities, requirements, outcomes, and success measures, distinguishing reusable capabilities from one-time requests.
• Define how customers discover, access, understand, and consume products; validate assumptions through feedback, prototypes, data analysis, and incremental delivery.
• Represent customers in development and lifecycle decisions.
• Partner with Data Engineering and Architecture to build scalable data products from source through transformation, modeling, quality validation, governance, and delivery.
• Collaborate with engineering to select fit-for-purpose delivery methods, such as datasets, APIs, events, files, analytical models, or other data services.
• Define product scope, ownership, service expectations, and consumption patterns; design for reuse rather than customer-specific pipelines, extracts, or point-to-point integrations.
• Address reliability, scalability, performance, and maintainability throughout the product lifecycle.
• Build data products for internal processes, analytics, reporting, digital experiences, operational systems, and AI, while understanding data needs for experiences serving employers, employees, partners, and other external consumers.
• Partner with digital and application teams to power customer-facing experiences and define downstream interfaces and service levels.
• Make data quality and trust core product requirements; partner with Governance, Security, Privacy, Risk, and Architecture to meet enterprise requirements and manage sensitive and regulated data.
• Establish business definitions, metadata, lineage, ownership, quality expectations, and access controls; define meaningful quality measures and thresholds with customers and engineering.
• Embed governance in the product experience so customers can confidently understand and use data.
• Own portfolio adoption and value realization, with measures tailored to each product’s purpose and customers.
• Track adoption, usage, quality, reliability, customer experience, and business outcomes; validate that products solve intended customer problems.
• Use telemetry, feedback, operational performance, and outcomes to guide the roadmap; improve, consolidate, replace, or retire underused and duplicative products.
• Communicate outcomes and portfolio health to business and technology leaders.
• Partner across Data Engineering, Data Platform Engineering, Architecture, Governance, Analytics, AI, application Product Management, and business teams.
• Translate business problems into product decisions without unnecessarily prescribing technical solutions; explain technical topics to business and executive audiences and align teams on outcomes.
• Resolve conflicts among customer needs, architecture, engineering constraints, governance, and enterprise priorities.
• Mentor Product Managers and Product Owners and mature Enterprise Data product-management practices.

Benefits

• Medical, dental, and vision benefits.
• 401(k) plan with matching company contributions.
• Annual incentive bonus plan.
• Paid time off, including 11 holidays, 2 wellness days, and 8 volunteer hours annually; PTO increases with tenure.
• Paid parental leave.
• Adoption and surrogacy assistance.
• Employee giving program that double matches donations to eligible nonprofits and schools.
• Opportunities for career growth and advancement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: High school or GED