Forward Deployed Engineer III, Data Cloud in London

On-site in London·Added today (last checked 13 Nov)

Google

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Our summary

Work mode: On-site, London
Experience: 5+ years

Skills

Nice to have

Google is hiring for this role in several places. This vacancy is in London.

Requirements

What we're looking for

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, Knowledge 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.

Nice to have

Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
Experience in prompt developing, model evaluation, and the creative application of AI.
Experience with Google Cloud data products (e.g., BigQuery, Managed Service for Apache Spark, Dataflow) or with equivalent platforms (e.g., Databricks, Snowflake, Palantir Foundry), and with open table formats (e.g., Apache Iceberg, Delta Lake).
Experience building applications on large language models 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.
Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

Pay & benefits

What you'll get

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Poland: zł364000 - zł373000 (PLN) + 15% bonus target + equity + benefits

Learn more about benefits at Google. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: London, UK; Warsaw, Poland.

The role

Google Cloud's Data Cloud team builds the data platform behind Google's agentic AI offerings: BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise.

As a Forward Deployed Engineer (FDE) on our Data Cloud team, you are an embedded builder who bridges the gap between frontier data solutions and production-grade reality for customers. You will function as an innovator-builder, working directly within customers environments to move data and machine learning workloads from idea to production on Google Cloud, leveraging experience with programming languages (e.g., PySpark, SQL, Python) and the data cloud ecosystem (e.g. BigQuery, Managed Spark, Spanner, AlloyDB). You will build the data foundations that agents depend on, and train and evaluate machine learning models on customer data. By embedding with strategic 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.

What you'll do

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.

Hiring process

4 steps3 interviewsa task to do6–8 weeks in all

Apply online
Assessments
Online test · Not every role
You may be asked to take the Google Hiring Assessment (workstyle skills) and, for some roles, role-related assessments such as a coding exercise.
Recruiter conversations
With a recruiter · Phone call · 1–2 sessions
One or two shorter conversations assessing key skills, sharing information about the organisation and team, and asking about your preferences such as location.
Project work
In your own time · Not every role
For some roles, a small project before interviews: a case study, writing or code samples.
Interviews
With a rotating cast of Googlers
A panel of structured interviews, over video or in person, with a rotating cast of Googlers; open-ended role-related questions scored against the same rubric for every candidate, no brainteasers.
Decision and offer · With a recruiter

Structured interviews: every candidate for a role is scored against the same rubric; no brainteasers.
AI tools are not permitted during interviews.
For some roles, pre-selected interviews happen virtually before final interviews onsite.