About the job
P-1381 At Databricks, we are passionate about enabling Data & 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, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for data interaction to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.
Responsibilities
Conduct deep-dive forensics into Spark core internals and the broader Databricks Data and AI ecosystem to resolve high-priority architectural failures and complex system anomalies.
Perform advanced code-level analysis and resource profiling to identify and mitigate systemic root causes, ensuring the stability and reliability of high-scale production workloads.
Optimise architectural performance across the Data and AI stack by refining execution parameters and enforcing best practice strategies to maximise resource efficiency and throughput.
Analyse global issue trends and patterns to partner directly with Product Engineering, influencing the product roadmap and driving initiatives that enhance long-term supportability.
Develop reproduction frameworks, automated workflows, and AI-driven diagnostic tools that translate complex backline findings into standardised resolution paths to empower and scale the broader organisation.
Qualifications
Minimum
10+ years of relevant experience, including deep expertise in one of the following three specialized tracks, along with proven experience in managing both customers and technical stakeholders.
Data Engineering Track: Expertise in large-scale big data solutions and ETL pipelines using Spark, Delta Lake, or Hive. Strong experience troubleshooting failures, diagnosing performance issues, and identifying root causes. Demonstrated problem-solving ability and understanding of data engineering best practices to ensure reliable, efficient workflows. Solid hands-on programming skills in Python, SQL, or Scala.
Product Supportability Track: Deep understanding of distributed system internals. Ability to perform code-level root-cause analysis and profiling (using metrics and heap/thread dumps) in Java, Scala, or Python. Proven record of contributing to bug fixes and mentoring other engineers.
AI Track: Experience with large-scale machine learning and generative AI systems, including LLM-based applications and agent-driven workflows. Strong grasp of model training, evaluation, and deployment in distributed environments. Experience managing the ML lifecycle, including governance and operationalisation. Skilled in diagnosing and optimising distributed ML workloads for performance and scalability.
Preferred
No preferred qualifications listed.