Forward Deployed Engineer III, Data Cloud, Google Cloud

Google Cloud’s Data Cloud team builds the data platform behind Google’s agentic AI offerings: BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise. As a Forward Deployed Engineer (FDE) on our Data Cloud team, you are an embedded builder who bridges the gap between frontier data solutions and production-grade reality for customers. You will function as an innovator-builder, working directly within customers environments to move data and machine learning workloads from idea to production on Google Cloud, leveraging experience with programming languages (e.g., PySpark, SQL, Python) and the data cloud ecosystem (e.g. BigQuery, Managed Spark, Spanner, AlloyDB). You will build the data foundations that agents depend on, and train and evaluate machine learning models on customer data.

What you'll do:

• Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL and Python on BigQuery, Managed Spark, Spanner and AlloyDB.
• Stand up working proofs with customers’ data, including de-identifying sensitive data (e.g., PCI, PHI).
• Build the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and the access controls that let custom agents act on trusted data.
• Train, evaluate and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.
• Refactor and migrate high-cost analytics, Spark and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.

Minimum qualifications: Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. 5 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including experience writing code in Python and SQL. Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Knowledge Catalog, AlloyDB). Experience taking production-grade data cloud solutions from conception to launch, including architecting data systems on cloud platforms (e.g., GCP) and building structured/unstructured pipelines. Experience training, evaluating, and deploying machine learning models in production.

Full details and application on the official Google careers page.