Sr. Manager - Data & AI Support Engineering

P-1388

As a Sr. Manager of the Data & AI Support Engineering team, you will lead and manage a team of Technical Solutions Engineers responsible for driving deep technical resolutions for complex customer issues across Spark, AI/ML, Streaming, and Lakehouse platforms. You will help customers realize business value from Databricks Ecosystem products through strong technical leadership, AI-first operational innovation and customer-centric execution. Mission 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. Outcomes Drive AI-first support transformation initiatives that improve resolution speed, case quality, operational efficiency and customer experience. Partner with Engineering and Product teams to operationalize AI-assisted diagnostics, observability insights, and intelligent escalation management for enterprise customers. Build and scale reusable AI-enabled workflows, automations, runbooks, and operational intelligence frameworks across the support organization. Lead and manage Technical Solutions Engineers, Team Leads, and support operations personnel across AMER support functions based out of the Dallas location. Own and improve operational KPIs including customer satisfaction, escalation management, backlog health, resolution efficiency, and support quality. Act as a senior escalation point for customers and internal teams while driving operational excellence and process optimization. Lead hiring, onboarding, mentoring, technical assessments, training, and career development for support engineers and technical leads. Conduct regular one-on-ones, annual review, and career development discussions with direct reports. Be a hands-on technical leader supporting complex issues related to Spark Core, Spark SQL, Structured Streaming, Delta Lake, Lakehouse architecture, and Databricks Runtime technologies. Guide customers on Spark runtime optimization, distributed systems performance, and best practices for scalable Data & AI workloads. Own Engineering JIRA escalations and proactively drive faster resolutions for customer-reported product issues. Maintain internal operational documentation, runbooks, and customer-facing knowledge base assets. Coordinate closely with Engineering and Backline Support engineering, customer experience intelligence teams to identify, reproduce, and report product defects effectively. Act as a strong customer advocate and collaborate with cloud partners to support mutual customer success. Participate in major incident management, escalation handling, on-call rotations, and critical production support activities. What we are looking for: 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. 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.BenefitsAt 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. Our Commitment to Diversity and Inclusion 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