Product Manager - Data & AI Platform

The Standard • Portland, OR

4d • $109,750 - $186,500 • Remote

About The Position

The Product Manager - Data & AI Platform owns the strategy, roadmap, adoption, and value realization of The Standard’s enterprise Data & AI Platform, with Azure Databricks as a core technology. The role connects customer needs, technical realities, and enterprise priorities, translating the engineering needs of Data Product and AI Product teams into a platform strategy that makes enterprise data easier to discover, access, process, govern, and use.

The Product Manager partners with Data Platform Engineering, Architecture, Data Governance, Security, Data Product teams, and AI Product teams to build a scalable, governed, self-service data foundation. The role develops strong working knowledge of Databricks and modern data-platform capabilities to prioritize investments, define reusable capabilities, establish self-service consumption patterns, and reduce friction for teams building Data and AI products. Success means those teams spend more time delivering business capabilities and less time building or navigating underlying data infrastructure.

This position requires product strategy, roadmap development, prioritization, stakeholder management, and experience serving internal engineering or technical consumers. The role also requires a strong understanding of cloud data platforms, lakehouse architecture, data governance, and how AI products consume enterprise data. The position is remote, as indicated by the #LI-Remote tag.

Requirements

• High School Diploma or GED required.
• 5–7 years of progressive experience in a business or technology environment.
• Strong product management, product strategy, roadmap development, prioritization, and stakeholder-management capabilities.
• Ability to translate technical consumer needs into platform capabilities and investment priorities.
• Experience managing products that support internal engineering or technical consumers.
• Experience using adoption, utilization, consumer feedback, and business outcomes to guide product decisions.
• Ability to balance near-term delivery needs with long-term platform strategy.
• Strong understanding of modern cloud data platforms and lakehouse architecture.
• Working knowledge of Azure Databricks and its role in enterprise data architectures.
• Familiarity with Databricks capabilities including Unity Catalog, Delta Lake, Workflows, notebooks, SQL, and PySpark.
• Understanding of data ingestion, transformation, orchestration, storage, serving, and data-product patterns.
• Understanding of data governance concepts including metadata, lineage, data quality, access control, privacy, and security.
• Familiarity with CI/CD, Git, APIs, event streaming, and cloud engineering practices.
• Ability to discuss data architecture and engineering tradeoffs with engineers and architects without personally implementing solutions.
• Understanding of how machine learning, GenAI, RAG, and agentic AI products consume enterprise data.
• Ability to identify where AI teams need reusable platform capabilities versus application-specific capabilities.
• Demonstrates ownership of product decisions, priorities, outcomes, and platform-consumer success.
• Develops technical depth in Databricks, data engineering, and AI architecture to understand tradeoffs and make informed decisions.
• Approaches the platform as a product, balancing consumer experience, reuse, scalability, adoption, and enterprise value.
• Continuously evaluates platform friction, adoption barriers, engineering feedback, and emerging technologies for improvement.
• Builds strong cross-functional partnerships and communicates informed perspectives constructively.

Nice To Haves

• Bachelor’s degree preferred.
• Experience managing data platforms, cloud platforms, developer platforms, internal technology products, or technically complex enterprise products is strongly preferred.
• Familiarity with MLflow, vector search, embeddings, graph data, unstructured data, model serving, and AI data pipelines is preferred.

Responsibilities

• Own the product strategy and roadmap for the enterprise Data & AI Platform, focusing on Databricks-based platform capabilities.
• Translate Data Product and AI Product team needs into platform capabilities, investment priorities, and measurable outcomes.
• Prioritize platform capabilities based on enterprise demand, reuse potential, consumer value, risk, and strategic alignment.
• Partner with Engineering and Architecture on the evolution of the Databricks lakehouse and its surrounding ecosystem.
• Balance new capabilities, modernization, technical debt, reliability, governance, and developer experience; clarify which capabilities belong to the platform versus consuming teams.
• Partner with Engineering to define reusable capabilities and standardized patterns for batch, streaming, and other enterprise data workloads.
• Drive governed data discovery, access, sharing, observability, quality, lineage, and reusable data-product patterns.
• Treat Data Product and AI Product teams as primary platform customers; understand their workflows, dependencies, pain points, and emerging needs.
• Establish platform consumption models, self-service golden paths, reusable patterns, reference implementations, and standardized platform services.
• Partner with AI Product teams to understand data needs for machine learning, GenAI, RAG, agentic AI, and other emerging workloads.
• Prioritize foundational capabilities that can be reused across teams and use consumer feedback and adoption data to improve the platform developer experience.
• Partner with Data Governance, Security, Privacy, Risk, and Architecture to incorporate governance requirements into platform capabilities.
• Help prioritize Unity Catalog capabilities supporting access control, metadata, discovery, and lineage; promote standardized approaches for sensitive and regulated data.
• Balance governance and risk requirements with efficient developer and data-consumer experiences, and encourage reuse of governed enterprise data over duplicated pipelines and siloed implementations.
• Own platform intake, onboarding, adoption, and consumer-feedback processes.
• Identify recurring engineering-team friction and translate it into roadmap opportunities; partner with Engineering on service levels, support models, documentation, and consumption experiences.
• Track platform adoption, reuse, onboarding experience, consumer satisfaction, reliability, and value realization; use telemetry and feedback to guide decisions and investments.
• Partner across Data Platform Engineering, Data Engineering, Architecture, Data Governance, Security, Analytics, Data Product Management, and AI Product Management.
• Translate complex technical topics into product decisions, priorities, tradeoffs, and business outcomes; facilitate decisions among teams with competing needs.
• Communicate platform strategy and roadmap to technical and business stakeholders and maintain alignment with enterprise Data & AI strategy.

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.
• Supportive, responsive management approach and 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.
• Competitive salary and employee-focused benefits supporting work-life balance.

What This Job Offers

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