About the company
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.
Responsibilities
- Lead and scale a world-class AI-first Data & AI Support Engineering organization that combines deep technical expertise, operational excellence, intelligent automation and customer-centric support to accelerate issue resolution, improve platform reliability and drive exceptional customer outcomes across enterprise-scale Data and AI workloads.
- Build AI-enabled support workflows and reusable automations to improve resolution speed and support quality.
- Use Agentic AI systems, logs, telemetry, observability platforms and internal systems to accelerate troubleshooting and root-cause analysis safely.
- Create reusable runbooks, prompts, and agentic workflows that scale operational efficiency across teams.
- Ensure strong AI governance, customer data safety, validation practices, auditability, and human-in-the-loop controls.
- Partner with Engineering and Product teams to drive AI-first support innovation and operational excellence.
Requirements
- 10+ years of experience designing, building, troubleshooting, and supporting large-scale Data & AI applications using Python, Java, Scala, Spark, or related distributed technologies.
- Strong work experience of AI-enabled support workflows, agentic AI systems, Claude Skills workflows, RAG architectures, vector databases and any other operational automation frameworks.
- Proven development/delivery experience at a production scale in Databricks tech stacks like Model serving, Lakehouse, Delta, DLT, Lakeflow, Lakebase platforms is a strong plus.
- Experience using AI tools for troubleshooting, root-cause analysis, observability analysis, and support workflow acceleration.
- Strong hands-on expertise in Apache Spark, Spark SQL, Structured Streaming, Delta Lake, and distributed data processing systems.
- Experience leading production-scale workloads across Big Data, Hadoop, AI/ML, Kafka, Streaming, Data Science, or Analytics platforms.
- Strong troubleshooting and performance tuning experience for Spark and JVM-based distributed systems, including memory management, garbage collection, heap analysis, and thread dump analysis.
- Hands-on experience with AWS, Azure, or GCP cloud platforms.
- Proven experience managing globally distributed technical teams and handling high-severity customer escalations.
- Strong analytical, debugging, problem-solving, and distributed systems troubleshooting skills.
- Excellent written and verbal communication skills with strong customer-facing leadership abilities.
- Strong organizational, multitasking, stakeholder management, and operational leadership capabilities.