Senior Product Operation Analyst

At Databricks, we are passionate about enabling Data and AI 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 solve 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.

Reporting to the Director, Product Operations, you will lead key projects, collaborate cross-functionally with stakeholders in Engineering, Data Science, HR, and Finance to ensure our systems deliver operational excellence while scaling with the company. This serves as the foundation for data-driven decision making that help drive growth for Databricks. Ideal candidates will have a strong background in business operations with a deep understanding of data management principles and practices.

In this role, you will be leading and supporting Serverless Networking platform projects where you will work across teams to drive launch of external customer-facing networking features and enhance the underlying networking infrastructure that supports our internal product teams.

• Build reports and dashboards for monthly, quarterly, and executive-level reporting
• Orchestrate jobs using Databricks Jobs and Lakeflow Declarative Pipelines with built-in data validation and reconciliations
• Establish coding, data management, and documentation standards and best practices
• Act as the connective tissue between Support, Product, Engineering, and Analytics to drive operational alignment, product readiness, and continuous improvement across support workflows
• Lead and execute strategic initiatives to scale global Support operations, including coverage models, KPI frameworks, and support process enhancements.
• Build and maintain dashboards that measure support effectiveness, surface product-driven case trends, and track customer experience across support channels.
• Support quarterly and annual planning cycles, including headcount, capacity modeling, and budget alignment in partnership with Finance and Workforce Management.
• Influence senior stakeholders by turning support and operational insights into clear, data-driven narratives that inform product and business decisions.

• 5+ years of experience in data and analytics, ideally at a SaaS company or large tech company
• Comfort navigating large datasets using SQL-based tools and delivering dashboards and BI visualizations
• Ability to translate complex data into actionable insights, the communicate and action those insights
• Proven track record driving cross-functional initiatives and collaborating across different organizations such as Product, Engineering, Data Science, GTM, and Support teams
• Excellent communication and stakeholder management skills, with an ability to influence without authority
• Strong process-orientation and systems thinker with bias for action
• Experience working in a high-growth, fast-paced environment and managing multiple priorities

• Familiarity with Databricks tools, especially for extract/transform/load functions
• Experience working on Support Operations for SaaS based companies.
• Familiarity with tools like Salesforce, Zendesk, Jira, Looker/Tableau, and operational workflows
• Operations, Consulting or Strategy experience

Local Pay Range

$122,100 — $162,700 USD

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

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