Forward Deployed Engineer, Data Cloud, Google Cloud

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Data Engineering

In short

As a Forward Deployed Engineer on Google Cloud's Data Cloud team, you will act as an embedded builder, taking data and machine learning workloads from idea to production on Google Cloud for customers. You will leverage your expertise in programming languages like PySpark, SQL, and Python, and data cloud ecosystem products such as BigQuery, Managed Spark, Spanner, and AlloyDB. The role involves building data foundations for agentic solutions, training, evaluating, and deploying ML models, and refactoring analytics and database workloads.

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.
Present 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, 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.

Requirements

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 coding experience in Python and SQL.
Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Data 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.

Desired Qualifications

Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
Knowledge of large language model (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Experience in prompt developing, model evaluation, and the creative application of AI.
Experience building applications on LLM over enterprise data (e.g., Gemini, agents, RAG, vector search) and evaluating them for quality, latency and cost.
Experience migrating analytics, Spark or database workloads between platforms with measured cost or performance outcomes.

Benefits

Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale.
World's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform.
Collaborative culture providing direct access to DeepMind's engineering and research minds.
Opportunity to be the catalyst for Google Cloud's mission, drive customer success, and define the new cloud era.

Skills

PythonSQLApache SparkPySparkBigQuerySpannerData CatalogAlloyDBMachine LearningGoogle CloudLangGraphCrewAIADKGeminiRAGVector Search

#Data Cloud#Google Cloud#Data Engineering#Machine Learning#AI#Agentic AI

About Google

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