Senior Supportability Product Specialist, Supportability and Support Platforms

CSQ
426R116

Location:

San Francisco or Mountain View

At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers and customer-obsessed, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines.

And we're only getting started.

The Supportability and Support Platforms (SSP) team focuses on improving the overall support experience for Databricks customers. Our mission is to enhance product supportability and streamline support capabilities, ensuring efficient and high-quality issue resolution. We aim to minimize customer friction by proactively addressing common issues and empowering our support teams with the best tools and processes. We are also Customer Zero for Databricks and use our Data and AI platforms to define how Support should be done in the era of Big Data and Gen AI.

You will play a crucial role in driving these initiatives, working closely with engineering, support, and data science teams to deliver impactful solutions.

• Drive the product vision and roadmap for Supportability and Support Platforms, focusing on improving product serviceability and support efficiency.
• Analyze customer support cases to identify key friction points and drive product improvements that reduce case volume and time to resolution.
• Develop and prioritize feature requests for product telemetry, observability, debuggability, and automation, collaborating with product engineering teams.
• Design and build tools and automation to optimize support processes and boost support engineer productivity.
• Create and implement knowledge management, training plans and best practices to enhance support team expertise and proficiency.
• Drive the offloading of appropriate service tasks from engineering to support teams, ensuring proper training and risk mitigation.
• Work with Data Science to analyze case data and identify patterns, driving improvements in product and support processes.
• Improve the containment rates of Databricks Assistant (GenAI Agentic Support Experience) by improving issue understanding and response generation and also improve Assistant to human agent handoff.
• Collaborate with cross‑functional teams, including engineering, support, and data science, to deliver impactful solutions.
• Act as the voice of the customer, integrating user feedback and support team insights to drive product improvements.

• 5+ years of experience as a Product Manager working on platform products, ideally within support or serviceability domains.
• Experience working with support operations and understanding the challenges faced by support teams.
• Strong analytical skills, with the ability to analyze case data and identify trends and patterns.
• Experience with data, AI, and cloud systems.
• Familiarity with tools like Salesforce and Jira.
• Ability to develop and drive product roadmaps based on customer and support team needs.
• Excellent communication and collaboration skills, with the ability to work effectively with cross‑functional teams.
• Passion for improving customer experience and empowering support teams.
• Experience programming in Python and Notebooks is a plus.

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non‑commissionable roles or on‑target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job‑related skills, depth of experience, relevant certifications and training, and specific work location.

Based on the factors above, Databricks anticipates utilizing the full width of the range. The total…