Forward Deployed Engineering Manager II, Data Cloud, Google Cloud

About the job

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 Engineering Manager, you will lead a Forward Deployed Engineering (FDE) team for Google Cloud’s Data Cloud. Your team will bridge the gap between frontier data solutions and production-grade reality for customers. You will take customers’ data and machine learning workloads from prototype to production on BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog. You will be a technical leader first: credible with CTOs and data platform leaders, able to review your team’s designs and code, and a strong judge of engineering talent.

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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Serve as the technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• Partner with Sales and Tech Leadership to define requirements for high-value opportunities, deploying specialized experts to key accounts.
• Lead technical hiring for FDE, evaluating data engineering expertise, systems engineering, and coding skills to build an exceptional engineering team.
• Identify skill gaps in emerging tech (MCP, tool-calling, and foundation models, building agents), ensuring the team maintains subject matter expertise in an evolving data stack.
• Collaborate with Product and Engineering to resolve blockers and translate field insights into roadmaps while building internal tools to drive organizational efficiency.

Qualifications

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience in cloud computing, technical sales engineering, or software engineering with cloud-native architectures.
• 2 years of experience managing a software engineering, FDE, or similar technical customer-facing team in a cloud computing environment.
• Experience reviewing designs and code in Python, PySpark and SQL.
• Experience designing platforms built on Data Cloud services (e.g., BigQuery, Managed Spark/Lightning Engine, Apache Iceberg (Borderless Lakehouse), Spanner, AlloyDB, Pub/Sub, Dataflow, Kafka).
• Experience building agentic AI systems on enterprise data (agents, retrieval, vector search, evaluation).

Preferred qualifications:

• Experience leading forward deployed engineering, field engineering or professional services organizations at a data platform or AI-first company or a hyperscaler’s data and machine learning services team.
• Experience engaging credibly with multi-level executives and with engineers.
• Experience with Google Cloud data products, or experience participating against them.
• Experience across a broad range of Data Cloud specializations, including Data Engineering, Data Science/Machine Learning, Application Development, Operations, and Analytical Databases/Data Management.
• Experience building end-to-end data pipelines, engineering features, and training/deploying machine learning models.
• Experience with RAG architectures, chunking/indexing strategies, vector databases, hybrid search, and the use of data governance for grounding agents in enterprise context.