New job, posted less than a week ago!
Job Details
Posted date: Nov 05, 2026
Location: Seattle, WA
Level: Senior
Description
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.As a Forward Deployed Engineer (FDE) on our Data Cloud team, you will be an embedded builder who bridges the gap between frontier data solutions and production-grade reality for customers. By embedding with accounts, you will serve a dual purpose: providing "white glove" deployment of complex systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Qualifications
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, Catalog, and 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.
Preferred qualifications: Experience building LLM applications (e.g., Gemini, RAG, vector search) and multi-agent frameworks (LangGraph, CrewAI, ADK) using advanced patterns (ReAct, delegation). Experience with GCP data products (BigQuery, Spark, Spanner, Dataplex) or equivalents (Databricks, Snowflake), and open table formats (Iceberg, Delta Lake). Experience migrating analytics, Spark, or database workloads between platforms with measured cost and performance outcomes. Skilled in prompt engineering, model evaluation, LLM-native metrics (latency, tokens/sec, cost), state management, and granular tracing.
Extended 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, Catalog, and 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.