[Hiring] Product Manager - Data & AI Platform @The Standard

Role Description

At The Standard, Agile Product Managers connect customer needs, technical realities, and enterprise priorities. They work across teams to make clear tradeoffs and ensure technology investments deliver practical, measurable value.

For the Data & AI Platform, the primary customers are the teams building enterprise data, analytics, and AI products. This role turns their engineering needs into a platform strategy that makes enterprise data easier to discover, access, process, govern, and use.

The Product Manager - Data & AI Platform owns the strategy, roadmap, adoption, and value realization of the enterprise data platform, with Azure Databricks as a core technology. This role partners closely with Data Platform Engineering, Architecture, Data Governance, Security, Data Product teams, and AI Product teams to build a scalable, governed, self-service data foundation.

Success in this role means Data and AI teams can spend more time delivering business capabilities and less time building or navigating underlying data infrastructure.

Key Responsibilities

• Data Platform Strategy & Roadmap:
  • 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.
• Databricks & Data Platform Capabilities:
  • 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.
• Enable Data & AI Product Teams:
  • 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.
• Data Governance & Enterprise Enablement:
  • 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.
• Platform Adoption & Developer Experience:
  • 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.
• Cross-Functional Product Leadership:
  • 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.

Qualifications

• A High School Diploma or GED is required. A Bachelor’s degree is preferred.
• 5–7 years of progressive experience in a business or technology environment.
• Experience managing data platforms, cloud platforms, developer platforms, internal technology products, or technically complex enterprise products is strongly preferred.
• 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.
• Familiarity with concepts such as MLflow, vector search, embeddings, graph data, unstructured data, model serving, and AI data pipelines is preferred.
• Ability to identify where AI teams need reusable platform capabilities versus application-specific capabilities.

Benefits

• A rich benefits package including medical, dental, vision and a 401(k) plan with matching company contributions.
• An annual incentive bonus plan.
• Generous paid time off including 11 holidays, 2 wellness days, and 8 volunteer hours annually — PTO increases with tenure.
• A supportive, responsive management approach and opportunities for career growth and advancement.
• Paid parental leave and adoption/surrogacy assistance.
• An employee giving program that double matches your donations to eligible nonprofits and schools.