Sr. Manager, Observability Platform Engineering

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.

As the Manager of the Observability Platform team, you will lead the engineers responsible for building and scaling the next generation of Databricks’ global observability systems. Your team enables every Databricks engineer—and our customers—to monitor, diagnose, and improve the reliability of our platform at massive scale. You will guide the strategy, architecture, and execution of systems that handle billions of active time series and process petabytes of logs daily, ensuring world‑class visibility into the health and performance of our products.

In this role, you will:

• Lead the design and development of the next-generation observability platforms that support billions of active time series and process petabytes of logs every day.
• Oversee infrastructure deployed across nearly a hundred cloud regions, empowering internal engineers and customers to effectively monitor the reliability and performance of Databricks.
• Drive the creation of advanced troubleshooting workflows that accelerate incident diagnosis, enabling engineers to rapidly derive insights from logs, metrics, and other telemetry.
• Leverage Databricks’ own data intelligence platform to push the boundaries of observability, setting new standards for industry‑leading incident analysis and reliability practices.
• Establish and uplevel monitoring and reliability best practices across Databricks engineering by developing opinionated tools and standards for structured logs, metrics, alerts, dashboards, and on‑call operations.
• Mentor, grow, and inspire engineers, fostering a culture of technical excellence and strengthening the broader observability community within Databricks.

What We Look For:

• 5+ years experience in the performance analysis discipline. Ability to identify performance issues, root cause problems, and be able to come up with potential solutions.
• Ability to build strong working relationships with developers and field engineers to facilitate triaging and mitigation of performance problems.
• At least 3 years of experience in managing top‑tier engineering teams
• BS in Computer Science (Masters or higher level of education preferred)
• Expertise in attracting, hiring and coaching engineers, who will meet the Databricks hiring standards. Experience up‑leveling teams via hiring top‑notch talent and growing existing team members.

Pay Range Transparency

Local Pay Range

$222,000 — $300,000 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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At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit .

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

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