Staff Software Engineer, Metrics and Logging
DatabricksMountain View, CaliforniaPosted 24 February 2026
Job Description
RDQ426R299
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 Logging Platform team plays a critical role in building scalable and efficient logging solutions that power observability across all Databricks services. As a Staff Software Engineer, you will drive the next generation of our logging infrastructure, enabling engineers across the company to gain deep insights into system behavior, troubleshoot issues efficiently, and optimize performance at scale.
The impact you'll have:
Build the future of logging at Databricks by designing and scaling our next-generation logging platform that processes petabytes of logs daily.
Develop and optimize log delivery pipelines to support low-latency, high-throughput log ingestion and querying, ensuring seamless observability across all Databricks services.
Enhance log accessibility and usability, developing tools that enable engineers to efficiently search, analyze, and derive insights from logs.
Collaborate with teams across Databricks to define best practices for structured logging, standardizing formats and improving the developer experience.
Improve reliability and cost-efficiency by optimizing log retention, indexing, and query performance to reduce operational overhead.
Mentor and uplevel engineers, fostering a culture of technical excellence within the team and broader observability community.
What we look for:
BS (or higher) in Computer Science, or a related field.
7+ years of production-level experience in one of: Scala, Rust, Go, Python, Java, C++, or similar languages.
Deep experience in software development, in large-scale distributed systems.
Experience driving complex projects involving multiple teams and stakeholders.
Familiarity with log collection, health monitoring, and observability tools.
Pay Range Transparency
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 compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here .
Zone 1 Pay Range
$190,000
$261,250 USD
About Databricks
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. To learn more, follow Databricks on
Twitter , LinkedIn
and
Facebook .
Benefits
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 https://www.mybenefitsnow. ... (truncated, view full listing at source)
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