Product Manager - Data & AI Platform

The next part of your journey is right around the corner — with The Standard. A genuine desire to make a difference in the lives of others is the foundation for everything we do. With a customer-first mindset and an intentional focus on building strong teams, we’ve been able to uphold our legacy of financial stability while investing in new, innovative technologies that support the needs of our customers. Our high-performance culture focused on operational excellence thrives thanks to remarkable people united by compassion and a customer-first commitment. Are you ready to make a difference?

Job Summary 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.

The Product Manager develops a strong understanding of Databricks and modern data platform capabilities and uses that knowledge to prioritize platform investments, define reusable capabilities, establish self-service consumption patterns, and reduce friction for teams building Data and AI products.

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 proj…