# Sr. Manager – Data & AI Support Engineering **Company**: Databricks **Location**: Texas **Work arrangement**: onsite **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Wikidata**: https://www.wikidata.org/wiki/Q18350420 **Apply**: https://job-boards.greenhouse.io/databricks/jobs/7914730002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_76258784-b2b ## Description 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 realise 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 organisation 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. Key Responsibilities: - 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 organisation. - 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 optimisation. - 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 optimisation, 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. 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 organisational, multitasking, stakeholder management, and operational leadership capabilities. ## Skills ### Required - Python - Java - Scala - Spark - Agentic AI systems - logs - telemetry - observability platforms - internal systems - Apache Spark - Spark SQL - Structured Streaming - Delta Lake - distributed data processing systems - Big Data - Hadoop - AI/ML - Kafka - Streaming - Data Science - Analytics platforms - AWS - Azure - GCP cloud platforms --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/databricks/jobs/7914730002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)