Forward Deployed Engineer III, Data Cloud, Google Cloud

Google•Kirkland, WA

11h•$174,000 - $252,000

About The Position

As a Forward Deployed Engineer on Google Cloud’s Data Cloud team, you will embed with customer accounts to deploy complex data solutions and turn field insights into feedback for Google Cloud’s product roadmap. The role focuses on bringing frontier data solutions into production, building data and machine-learning workloads, and helping customers solve business problems using Google Cloud’s AI and data platform. You will work with products including Gemini and Vertex AI, and collaborate with Google Cloud’s Go-To-Market team and engineering and research colleagues.

Requirements

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

Nice To Haves

Experience building LLM applications such as Gemini, RAG, and vector search, and multi-agent frameworks such as LangGraph, CrewAI, and ADK using advanced patterns such as ReAct and delegation.
Experience with GCP data products such as BigQuery, Spark, Spanner, and Dataplex, or equivalents such as Databricks and Snowflake, and open table formats such as Iceberg and Delta Lake.
Experience migrating analytics, Spark, or database workloads between platforms with measured cost and performance outcomes.
Skill in prompt engineering, model evaluation, LLM-native metrics such as latency, tokens per second, and cost, state management, and granular tracing.

Responsibilities

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 such as PCI and PHI.
Build data foundations for agentic solutions, including lakehouse architectures, metadata and semantic context, and access controls that allow custom agents to use trusted data.
Train, evaluate, and deploy machine-learning and agentic components when they improve customer business metrics, 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.