Join Google Cloud’s Data Cloud team and be at the forefront of AI innovation. You will lead a Forward Deployed Engineering (FDE) team, transforming cutting-edge data solutions into production-ready realities for customers. This role offers the opportunity to architect and deploy data and machine learning workloads on Google's advanced platforms like BigQuery, Managed Spark, and AlloyDB, directly contributing to the AI revolution for businesses globally. You'll leverage Google's unparalleled AI portfolio, including Gemini models and the Vertex AI platform, in a collaborative environment that provides direct access to DeepMind's engineering and research expertise.

As a technical leader, you will establish high engineering standards, including code quality, architectural best practices, and performance benchmarks. You will collaborate closely with business and technical leadership to define requirements for impactful opportunities and deploy specialized expertise to key customer accounts. A significant part of your role will involve leading technical hiring for FDEs, assessing expertise in data engineering, systems engineering, and coding to build an exceptional team. You'll also stay ahead of emerging technologies, identify skill gaps, and collaborate with product and engineering teams to translate field insights into product roadmaps and enhance organizational efficiency.

A Bachelor’s degree in Engineering, Computer Science, or a related field, or equivalent practical experience, is required. Candidates should possess at least 8 years of experience in cloud computing, technical business engineering, or software engineering with cloud-native architectures. Additionally, 2 years of management experience leading software engineering, FDE, or a customer-facing technical team in a cloud environment is necessary. Proficiency in reviewing code and designs using Python, PySpark, and SQL is essential. Experience building platforms on Data Cloud services (e.g., BigQuery, Managed Spark, Spanner, AlloyDB) and developing agentic AI systems for enterprise data is also crucial.