Product Manager - Data & AI Platform

The Standard is hiring a Product Manager to own the strategy, roadmap, adoption, and value realization of its enterprise Data & AI Platform, centered on Azure Databricks. The role partners with engineering, architecture, governance, security, and data and AI product teams to deliver scalable, governed, self-service capabilities that help teams build enterprise data and AI products.

Responsibilities:

• Own the product strategy and roadmap for the enterprise Data & AI Platform, with a strong focus on Databricks-based platform capabilities
• Translate the needs of Data Product and AI Product teams into platform capabilities, investment priorities, and measurable outcomes
• Prioritize platform capabilities based on aggregate 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 platform capabilities, modernization, technical debt, reliability, governance, and developer experience
• Maintain a clear distinction between capabilities owned by the platform and capabilities owned by consuming product teams
• Develop strong working knowledge of Azure Databricks, Delta Lake, Unity Catalog, Databricks Workflows, Lakehouse architecture, Azure Data Lake, SQL, Python/PySpark, APIs, streaming, and related data-platform technologies
• Understand common data engineering patterns including ingestion, transformation, orchestration, storage, governance, data quality, lineage, and serving
• Partner with Engineering to define reusable platform capabilities and standardized patterns for batch, streaming, and other enterprise data workloads
• Drive platform capabilities supporting governed data discovery, access, sharing, observability, quality, and lineage
• Help establish scalable patterns that allow data products to move from source ingestion through curated, reusable enterprise data
• Treat Data Product and AI Product teams as primary platform customers and deeply understand their workflows, technical dependencies, pain points, and emerging needs
• Establish platform consumption models that allow teams to build products without creating bespoke infrastructure
• Champion self-service golden paths, reusable patterns, reference implementations, and standardized platform services
• Drive capabilities that enable product teams to discover and consume trusted enterprise data
• Partner with AI Product teams to understand data requirements for machine learning, GenAI, RAG, agentic, and other emerging AI workloads
• Prioritize foundational capabilities that can be reused across multiple teams rather than solving repeatedly for individual projects
• Use consumer feedback and adoption data to continuously improve the platform developer experience
• Partner with Data Governance, Security, Privacy, Risk, and Architecture to ensure governance requirements are incorporated into platform capabilities
• Understand and help prioritize Unity Catalog capabilities supporting access control, metadata, discovery, and lineage
• Promote standardized approaches for managing sensitive and regulated enterprise data
• Balance governance and risk requirements with the need for an efficient developer and data-consumer experience
• Drive adoption of reusable, governed enterprise data rather than duplicated pipelines and siloed implementations
• Own platform intake, onboarding, adoption, and consumer feedback processes
• Champion simple, documented, self-service paths for common Data and AI development scenarios
• Identify recurring friction experienced by engineering teams and translate those problems into roadmap opportunities
• Partner with Engineering to define appropriate service levels, support models, documentation, and consumption experiences
• Track platform adoption, reuse, onboarding experience, consumer satisfaction, reliability, and value realization
• Use platform telemetry and consumer feedback to inform product decisions and investment priorities
• Partner closely with Data Platform Engineering, Data Engineering, Architecture, Data Governance, Security, Analytics, Data Product Management, and AI Product Management
• Translate complex technical topics into clear product decisions, priorities, tradeoffs, and business outcomes
• Facilitate decisions where multiple consuming teams have competing needs
• Communicate the platform roadmap and strategy to technical and business stakeholders
• Maintain alignment between platform investment and enterprise Data & AI strategy

Requirements:

• A High School Diploma or GED is 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
• Demonstrated ability to translate technical consumer needs into platform capabilities and investment priorities
• Experience managing products supporting 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 such as 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 needing to personally implement the 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
• A Bachelor's degree is preferred
• Experience managing data platforms, cloud platforms, developer platforms, internal technology products, or technically complex enterprise products is strongly preferred
• Familiarity with concepts such as MLflow, vector search, embeddings, graph data, unstructured data, model serving, and AI data pipelines is preferred