Join Google Cloud's Data Cloud team as a Forward Deployed Engineer, a critical role focused on bringing cutting-edge data and AI solutions to life for our customers. You'll be an integral part of building the data platform that powers Google's generative AI offerings, including BigQuery, Spanner, and AlloyDB, integrated with Gemini Enterprise.

This position offers a unique opportunity to work directly within customer environments, acting as an innovator-builder to transition complex data and machine learning workloads from concept to production on Google Cloud. You will leverage your expertise in programming languages like PySpark, SQL, and Python, along with your knowledge of the data cloud ecosystem, to establish robust data foundations for agentic AI.

As an embedded engineer, you will provide high-touch deployment support for sophisticated systems and serve as a vital conduit for feedback. Your real-world insights will directly influence the future product roadmap of Google Cloud, ensuring our offerings remain at the forefront of technological advancement. This role is instrumental in driving customer success and shaping the new era of cloud computing.

Develop and deploy production-ready data and machine learning solutions collaboratively with clients. This includes writing, testing, and optimizing PySpark, SQL, and Python code for platforms such as BigQuery, Managed Spark, Spanner, and AlloyDB.

Present functional proofs-of-concept using customer data, ensuring sensitive information like PCI and PHI is appropriately de-identified.

Construct the essential data infrastructure for agentic AI, encompassing lakehouse architectures, metadata management, semantic context, and secure access controls that empower custom agents.

Train, evaluate, and deploy machine learning and agentic components designed to enhance key customer business metrics, ensuring comprehensive evaluation and monitoring frameworks are in place.

Refactor and migrate substantial analytics, Spark, and operational database workloads from alternative platforms to Google Cloud engines, delivering measurable improvements in price-performance.

A Bachelor's degree in Engineering, Computer Science, or a related field, or equivalent practical experience, is required.

Possess at least 5 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, with demonstrated proficiency in Python and SQL.

Familiarity with data cloud ecosystem products, technologies, or languages such as Apache Spark, PySpark, BigQuery, Spanner, Data Catalog, and AlloyDB is essential.

Proven experience in taking data cloud solutions from initial conception through to production launch, including designing data systems on cloud platforms (e.g., GCP) and developing structured/unstructured data pipelines.

Demonstrated ability to train, evaluate, and deploy machine learning models in production environments.