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.
- 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.
- 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.
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 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 equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Skills Required
- 10+ years designing, building, troubleshooting, and supporting large-scale Data & AI applications using Python, Java, Scala, Spark, or related distributed technologies
- Experience with AI-enabled support workflows, agentic AI systems, Claude Skills workflows, RAG architectures, and vector databases
- Production-scale experience with Databricks tech stacks (Model serving, Lakehouse, Delta, DLT, Lakeflow, Lakebase)
- Experience using AI tools for troubleshooting, root-cause analysis, and observability-driven support workflow acceleration
- 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, Kafka, Streaming, AI/ML, Data Science, or Analytics platforms
- Troubleshooting and performance tuning for Spark and JVM-based systems, including memory management, garbage collection, heap and thread dump analysis
- Hands-on experience with cloud platforms (AWS, Azure, or GCP)
- Proven experience managing globally distributed technical teams and handling high-severity customer escalations
- Excellent written and verbal communication and strong customer-facing leadership skills
- Strong organizational, multitasking, stakeholder management, and operational leadership capabilities
- Participation in major incident management, escalation handling, on-call rotations, and critical production support
- Familiarity with JIRA and observability/telemetry platforms for escalation and diagnostics
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants and RSUs are a major part of total compensation and are highlighted for meaningful upside potential. Stock-based awards and refreshers contribute to strong overall pay positioning across senior technical and go-to-market roles.
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Healthcare Strength — Medical, dental, and vision coverage are complemented by mental-health resources, an EAP, and wellness reimbursements. Health benefits are consistently framed as comprehensive and competitive.
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Parental & Family Support — Paid parental leave for all parents, fertility support, and backup care options provide tangible assistance for family needs. Hybrid work norms and team-day structure further ease coordination for caregivers.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
