Forward Deployed Engineer IV, Data Cloud, Google Cloud
About the role
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
[https://www.google.com/about/careers/applications/benefits/].
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.
Responsibilities
- Build and ship production data and machine learning workloads with customers using PySpark, SQL, Python, BigQuery, Managed Spark, Spanner, and AlloyDB.
- Create customer proofs of concept using their data, including de-identifying sensitive information.
- Build data foundations for agentic solutions, including lakehouse architectures, metadata, semantic context, and access controls.
- Train, evaluate, and deploy machine learning and agentic components with appropriate evaluation and monitoring.
- Refactor and migrate analytics, Spark, and operational database workloads to Google Cloud and measure price-performance outcomes.
Qualifications
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- At least 8 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including coding 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 cloud data architecture and structured or unstructured pipelines.
- Experience training, evaluating, and deploying machine learning models in production.
- Preferred experience building LLM applications and multi-agent frameworks, and using advanced patterns such as ReAct and delegation.
- Preferred experience with cloud data products, open table formats, and migrating analytics, Spark, or database workloads between platforms.
- Preferred skills in prompt engineering, model evaluation, LLM-native metrics, state management, and granular tracing.
Benefits
- 20% bonus target
- Equity
- Benefits
Skills mentioned
About Google
A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone. Check out our career opportunities at goo.gle/3DLEokh
H-1B sponsorship history
Historical employer filing data was found for Google. The employer record includes 57,181 historical certified applications. This is employer-level history, not a guarantee that this role currently offers sponsorship.