Staff Software Engineer - Streaming

We're seeking an experienced Staff Engineer to drive the technical direction of Spark Structured Streaming, spanning both open source and Databricks-specific components. Your mission is to make Spark Structured Streaming the state-of-the-art stream processing engine — adding advanced capabilities such as sophisticated state management and new operators, while re-imagining the engine's architecture to drive improvements for latency, throughput, and cost.

• Set and drive the technical vision for Spark Structured Streaming across OSS and the Databricks Data Intelligence Platform
• Design and build core engine capabilities — state management, new operators, and architectural improvements to latency and throughput
• Raise the bar for engineering quality and operability, building software that is not just high quality but easy to run in production
• Make company-wide impact by driving stream processing adoption across the Databricks product portfolio
• Guide long-term architecture and technical-debt decisions, balancing them against the product roadmap
• Mentor and technically lead engineers on the team, and partner with the Engineering Manager to attract and grow top-tier talent

• BS (or higher) in Computer Science or a related technical field, or equivalent practical experience
• 8+ years building related systems — big-data ecosystems, Apache Spark, or database internals
• A passion for database systems, storage systems, distributed systems, language design, or performance optimization
• Comfortable working toward a multi-year vision with incremental deliverables
• A track record of delivering features while maintaining a high bar for operational excellence and engineering quality
• Comfortable working cross-functionally with product management and directly with customers, with the ability to deeply understand the product and customer personas

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.

Local Pay Range

$182,400—$247,000 USD

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 click here.

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 turn deep data insights into better business outcomes.

We are the Spark Structured Streaming team, responsible for building stream processing into Apache Spark and the Databricks Data Intelligence Platform. Stream processing is still in its early days, and we're here to build not only state-of-the-art streaming technology but also a best-in-class managed offering for customers to run their streaming workloads.

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.