Forward Deployed Engineer IV, Data Cloud, Google Cloud

Google; Chicago (IL); On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off: 20 days/year

Job summary

Google is seeking a Forward Deployed Engineer IV to embed with customers, building production-grade data and ML solutions on Google Cloud for real-world impact.

You will work across data cloud products (BigQuery, Spark, Spanner, AlloyDB) and help scale end-to-end pipelines, with heavy collaboration across Go-To-Market teams and customers. Eligible locations include SF, Austin, Chicago, and more.

Qualifications

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
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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 (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.

Skills

Python

SQL

Spark

PySpark

BigQuery

Spanner

AlloyDB

ML production

Education

Bachelor’s degree in Engineering or CS

Tools

Databricks

Snowflake

Job description

Forward Deployed Engineer IV, Data Cloud, Google Cloud

corporate_fare

Google

place

San Francisco, CA, USA; Austin, TX, USA; +5 more

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X

In most instances, this position requires in-person interviews as part of the hiring process.

Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Austin, TX, USA; Chicago, IL, USA; Kirkland, WA, USA; New York, NY, USA; Seattle, WA, USA; Sunnyvale, CA, USA.

Minimum qualifications

• 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 (e.g., Apache Spark, PySpark, BigQuery, Spanner, Catalog, 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.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

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 (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.