Sr. Product Manager - Technical

Databricks Databricks · Data AI · New York, NY · Product

This role is for a Sr. Technical Product Manager at Databricks, focusing on either OLTP Databases or Data Governance. The candidate will work with engineering and field teams to define product roadmaps, identify improvements, and ensure features deliver outstanding user experiences. For the governance focus, a key responsibility is enabling secure and compliant access for AI agents to the Databricks Data Intelligence Platform. The role requires a strong technical background, empathy for customers, domain expertise, Python/SQL skills, and experience with systems design and architecture. Experience with AI-assisted development tools is mentioned.

What you'd actually do

  1. Identify and drive impactful product improvements in your domain of expertise
  2. Define and run performance benchmarks (OLTP focus) or governance best practices and reference architectures (governance focus)
  3. Shape and prioritize a meaningful product roadmap
  4. Support go-to-market efforts and guide product adoption
  5. For governance focus: define processes and mechanisms for how AI agents securely and compliantly access the Databricks Data Intelligence Platform

Skills

Required

  • 5+ years of experience with a strong, hands-on technical background
  • Strong empathy for customers across full spectrum of Data Platform users
  • Deep domain expertise in one of the following: OLTP Databases or Data Governance
  • Experience evaluating and comparing technologies across dimensions such as performance, reliability, governance, and compliance
  • Strong Python and SQL skills
  • Experience with systems design and architecture
  • Proven ability to work effectively across product, engineering, and technical field teams

Nice to have

  • Experience using AI-assisted development tools

What the JD emphasized

  • deeply understand both functional and non-functional requirements
  • technical field teams
  • product and engineering
  • customer PoCs, benchmarks, and real-world implementations
  • performance, reliability, governance, and compliance
  • AI agents