Bachelor's degree in Engineering, Computer Science, or related field, or equivalent experience.
5 years of experience in software engineering, data engineering, or technical consulting, with proficiency in Python and SQL.
Familiarity with data cloud technologies like Apache Spark, BigQuery, and AlloyDB.
Experience in architecting and launching production-grade data cloud solutions on platforms like GCP.
Proven ability to train and deploy machine learning models in production.
Responsibilities
Build and deploy production data and machine learning workloads using PySpark, SQL, and Python.
Create working proofs with customer data, ensuring sensitive data is de-identified.
Establish data foundations for agentic solutions, including lakehouse architectures and access controls.
Train and deploy machine learning components to enhance customer business metrics, with monitoring in place.
Migrate high-cost analytics and database workloads to Google Cloud, ensuring cost-effectiveness.
Benefits
Access to advanced AI technologies and tools.
Collaborative culture with direct access to DeepMind's engineering and research teams.
Opportunity to influence Google Cloud's product roadmap based on customer insights.
Participation in a mission-driven environment focused on customer success.
Full Job Description
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.
About the job 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.
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.
About Google
Google is a multinational technology company that specializes in Internet-related services and products. These include online advertising technologies, search engine, cloud computing, software, and hardware. Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. The company has grown tremendously since then and has become one of the most valuable companies in the world. Google's mission is to organize the world's information and make it universally accessible and useful.
