Forward Deployed Engineer IV, Data Cloud, Google Cloud

Google•Kirkland, WA

12h•$207,000 - $300,000

About The Position

As a Forward Deployed Engineer on Google Cloud’s Data Cloud team, you will embed with customer accounts to build and deploy production-grade data and machine learning solutions. The role combines hands-on deployment of complex systems with gathering field insights that inform Google Cloud’s product roadmap. You will work with customers on data platforms, analytics, machine learning, and agentic solutions, using Google Cloud products and frontier AI capabilities. Individual pay is determined by job-related skills, experience, and relevant education or training.

Requirements

Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 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 such as Apache Spark, PySpark, BigQuery, Spanner, Catalog, or 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, or vector search, and multi-agent frameworks such as LangGraph, CrewAI, or ADK using advanced patterns such as ReAct and delegation.
Experience with GCP data products such as BigQuery, Spark, Spanner, or Dataplex, or equivalents such as Databricks or Snowflake, and open table formats such as Iceberg or 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 the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and access controls that let custom agents act on trusted data.
Train, evaluate, and deploy machine learning and agentic components where 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.