Senior Product Manager - Enterprise Data Products

The Standard • Portland, OR

23h • $137,000 - $233,000 • Remote

About The Position

The Senior Product Manager owns the strategy, roadmap, prioritization, lifecycle, adoption, and business outcomes for an enterprise data-product portfolio at The Standard. The role connects customer needs, business priorities, and technology, turning enterprise data into reliable products for internal business teams, analysts, operational systems, digital experiences, AI solutions, partners, and other consumers. The team follows a data-as-a-product approach and delivers analytical and transactional data products across the data lifecycle.

The position works with internal and external customers and stakeholders to translate data needs for decisions, experiences, processes, and downstream products into defined offerings with measurable outcomes. It partners closely with 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; resolves competing needs among customers, architecture, engineering constraints, governance, and enterprise priorities; and helps mature Enterprise Data product-management practices. The role is remote as indicated by the job posting’s #LI-Remote tag.

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.
• Ownership of 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 product vision into executable priorities and define outcomes and measures of 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 understanding of emerging Data and AI capabilities to customer needs and roadmap decisions.

Nice To Haves

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

Responsibilities

• Own the enterprise data-product portfolio vision, strategy, lifecycle, and forward-looking roadmap.
• Translate customer problems, enterprise strategy, regulatory requirements, and technology opportunities into product 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.
• Understand customer workflows, decisions, experiences, and data needs through continuous discovery with business, operations, analytics, application, digital-product, and partner stakeholders.
• Translate customer problems into capabilities, requirements, outcomes, and success measures; distinguish 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 product development and lifecycle decisions.
• Partner with Data Engineering and Architecture to build scalable products, guiding them from source through transformation, modeling, quality validation, governance, and delivery.
• Work with engineering to select appropriate delivery methods, including 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; understand 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.
• Own portfolio adoption and value realization, with measures tailored to product purpose and customers.
• Track adoption, usage, quality, reliability, customer experience, and business outcomes; regularly 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.
• 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.
• Opportunities for career growth and advancement.
• Paid parental leave.
• Adoption and surrogacy assistance.
• Employee giving program that double matches donations to eligible nonprofits and schools.
• Drug- and alcohol-free work environment.

What This Job Offers

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