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Customer-facing technical leader who architects, builds, and optimizes large-scale data engineering and lakehouse solutions on Databricks. Supports pre-sales and delivery with POCs, performance/load testing, workload sizing, migrations, observability, and adoption through workshops and community engagement.

Own the vision, strategy, and roadmap for Databricks’ AI-first learning platform. Build contextual guidance, adaptive learning paths, learner models, assessments, skills graphs, analytics, and personalization tools. Partner with engineering, UX, curriculum, education, and go-to-market teams to deliver scalable learning experiences for customers, partners, and employees. Define metrics, understand skills gaps, engage users, and improve platform adoption through embedded learning capabilities.

Lead technical strategy for customer accounts by designing scalable data, AI, streaming, and analytics architectures on Databricks. Advise senior technical stakeholders, drive platform adoption and consumption growth, conduct architecture discussions, build competitive solutions, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, public-cloud deployment experience, distributed data systems expertise, and excellent technical communication.

Leads and scales a customer-facing AI Forward Deployed Engineering team, overseeing hiring, mentoring, strategic AI initiatives, enterprise engagements, and production AI delivery. Builds executive customer relationships, partners with Sales, Product, Engineering, and GTM, influences product strategy through customer insights, manages account and services outcomes, and represents Databricks as an AI thought leader.

Guide enterprise customers in designing, securing, administering, and deploying Databricks environments on AWS. Architect scalable infrastructure, networking, identity, and security solutions; support pre-sales engagements through proofs of concept and technical content; and provide technical leadership, training, tutorials, and community contributions. The role requires expertise in AWS, distributed data architectures, infrastructure automation, programming, SQL, and customer-facing technical consulting, with travel up to 30%.

Provides technical leadership and pre-sales guidance for Databricks Customer Lake implementations. Designs customer data, CDP, Lakehouse, identity resolution, audience activation, personalization, and agentic AI solutions; leads executive engagements, workshops, demos, POCs, and technical collateral. Partners with account teams, GSIs, and consulting organizations to drive adoption, influences product roadmaps, handles tier-three escalations, improves qualification processes, and mentors field teams.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

Leads global marketing planning, investment allocation, capacity modeling, and target setting. Builds and manages a new team while redesigning marketing workflows for AI and agent-based execution. Drives organization-wide adoption, establishes standards for prompts and context, and implements AI governance around quality, risk, privacy, and responsible use. Partners with Marketing, Finance, Sales Strategy, and data and platform teams to improve pipeline efficiency, time to market, and growth across business units.

Drive enterprise SaaS sales for Databricks by prospecting, identifying buying centers, developing account strategies, communicating platform value, guiding negotiations, closing new accounts, and expanding existing relationships. The role requires consultative selling of cloud, big data, and AI technologies, engagement with CIOs and decision-makers, quota attainment, and collaboration across teams.

Leads platform incident investigations, coordinates detection and resolution, performs root cause analyses, and designs customer-focused monitoring, alerting, and observability workflows. Builds automation, improves platform reliability, mentors engineers, and participates in on-call rotations. The role requires cloud, container orchestration, monitoring, logging, alerting, incident management, and production automation expertise.