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Lead customer-facing projects to design, build, and productionize data and AI solutions on Databricks. Own architecture, implement end-to-end data pipelines, ML/AI model integration, and user-facing applications; collaborate with engineering, product, and support to deliver scalable, secure systems and reusable accelerators while driving customer adoption and measurable outcomes.

Lead a team of researchers to improve large-scale machine learning, focusing on efficiency, optimization, and scaling techniques for AI models.

The role involves leading complex, cross-functional programs in enterprise software, influencing roadmap decisions, driving execution across organizations, and ensuring successful product launches and customer adoption.

Owner/operator for Procure-to-Pay subprocesses: map and redesign workflows, build and deploy automations and data products (SQL/Python/Databricks), maintain P2P datasets and dashboards, drive adoption through change management, and prioritize intake and backlog across Procurement.

Provide technical leadership in pre-sales for Databricks, designing big data and ML architectures, implementing proofs-of-concept across data engineering, data science, and SQL workflows, validating cloud and third-party integrations, and building technical champions and community contributors.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.

Partner with sales to design and implement Databricks-based big data architectures for named accounts. Consult on data engineering, data science, ML, and SQL workflows; build proofs-of-concept; validate integrations with cloud services and third-party tools; collaborate with product and solutions teams; promote and recruit contributors for Databricks open-source projects. Travel to customer sites in your region as needed.

Act as the field product manager for high-value AI engagements: partner with Sales to scope C-suite problems, define MVPs and PRDs, prioritize engineering backlogs, run agile delivery, lead launch and adoption, and hand off ongoing operations while proving ROI.

As a Staff Software Engineer, you will build distributed data systems, focusing on reliability and performance for large-scale data processing, using technologies like Apache Spark™ and Delta Lake.

Build next generation distributed data storage and processing systems, including projects like Apache Spark and Delta Lake, while focusing on customer value and handling complex data engineering pipelines.