Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- A rich benefits package including medical, dental, vision and a 401(k) plan with matching company contributions
- An annual incentive bonus plan (eligibility to participate is subject to the rules governing the program and plan; any award depends on a variety of factors including individual and organizational performance)
- Generous paid time off including 11 holidays, 2 wellness days, and 8 volunteer hours annually — PTO increases with tenure
- A supportive, responsive management approach
- Opportunities for career growth and advancement
- Paid parental leave
- Adoption/surrogacy assistance
- An employee giving program that double matches your donations to eligible nonprofits and schools
- Remote work in the USA
Company Overview
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