Jobs at Databricks

Pre-sales Solutions Architect for Databricks Lakebase focused on driving product adoption and architecture for transactional (OLTP) systems within Lakehouse, Big Data and AI ecosystems. Partner with account teams to design engagement strategies, deliver demos/workshops, enable customers, influence product roadmap, and serve as tier-3 escalation for mission-critical technical issues.

Manage a strategic enterprise account within a large conglomerate, building executive relationships and driving value-based growth. Expand Databricks adoption across business units and use cases, exceed pipeline and revenue targets, forecast opportunities in Salesforce, and sell the Databricks Data Intelligence Platform powered by Apache Spark and Delta Lake. The role requires guiding complex deals, communicating technical value, and collaborating with customers, system integrators, and internal teams.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Design and architect production-grade ML and GenAI solutions on Databricks, including RAG and agentic systems, MLOps pipelines, training/inference optimization, model monitoring, and customer-facing technical enablement and mentorship.

Drive revenue for Lakebase by identifying, qualifying, and closing opportunities, engaging with technical and business stakeholders, and orchestrating proof-of-value for operational workloads.

Lead technical pre-sales and solution architecture for Databricks Lakehouse customers. Advise sales and product teams, design big data and ML architectures, implement proofs-of-concept, validate cloud integrations, and build technical champions across strategic accounts.

Manage and scale APJ Partnerships team's GTM strategy using data-driven insights, support pipeline/forecast calls, and conduct ROI analyses.

As an Account Executive, you'll drive business growth in India by prospecting for new customers and managing existing accounts, leveraging Databricks' platform to create value and close deals.

Design and run a scalable, data-driven partner enablement operating model across AMER, EMEA, and APJ. Lead governance, planning cadences, QBRs/MBRs, dashboards, automation and AI-enabled workflows, cross-functional execution, escalation resolution, and continuous process improvements to increase predictability and scale.

Provide technical leadership and pre-sales architecture for Databricks Lakehouse solutions. Drive account strategies, design big data and ML architectures, build POCs, validate integrations with cloud and third-party tools, and collaborate with product and engineering to deliver data-driven outcomes.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture, design, coding, deployment, and technical delivery for scalable end-to-end systems involving data pipelines, machine learning models, and applications. Advise enterprise stakeholders, guide adoption, integrate customer systems, resolve technical issues, and contribute reusable frameworks and product feedback. The role requires strong software and data engineering expertise, customer empathy, project delivery skills, and approximately 20% travel.

Build and productionize customer-facing data and AI solutions on Databricks. Own architecture, design, implementation, deployment, and technical delivery of scalable systems spanning data engineering, machine learning, AI models, pipelines, and applications. Guide enterprise customers, collaborate with engineering and product teams, resolve implementation issues, document solutions, and create reusable accelerators. The role requires customer immersion, stakeholder management, technical project delivery, and approximately 20% travel.

Build and productionize customer data and AI solutions on Databricks. Own architecture, design, coding, deployment, and integration of data pipelines, ML/AI models, and applications. Lead customer technical projects, guide architecture decisions, manage delivery scope and timelines, train stakeholders, resolve implementation issues, and create reusable frameworks. Collaborate with engineering, product, project management, support, and customer teams while traveling to customers approximately 20% of the time.

Build trusted client relationships and demonstrate Databricks solutions through hands-on evaluations, Spark programming, proofs of concept, workshops, and technical presentations. Advise customers on big data analytics, solution architecture, data engineering, cloud integrations, and business value. Author reference architectures, how-to guides, and demo applications while developing toward independent Solutions Architect responsibilities.

Manages and mentors a team of data engineers and architects, provides technical guidance, oversees customer Databricks projects, leads reusable tooling development, handles escalations, partners with stakeholders, improves service offerings, and supports customer success throughout sales and delivery.

Own the non-technical lifecycle of Field Deployment engagements: partner with sales to define C-suite value and MVPs, write PRDs, prioritize engineering backlogs, drive agile delivery, act as hands-on QA, manage launches and adoption, and scope ongoing maintenance while identifying growth opportunities.

Serve as the field product manager for FDE engagements: scope C-suite value, define pilot MVP and PRD, prioritize engineering backlog, run agile delivery, manage launch and adoption, and hand off ongoing maintenance and growth opportunities.

Customer-facing senior engineer who architects and delivers production-ready big data and AI solutions on Databricks across cloud platforms. Advises customers, builds reusable automation, designs reference architectures, supports migrations, and partners with engineering and support to drive product improvements and adoption.