Forward Deployed Engineering Manager, Data Cloud, Google Cloud

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience in cloud computing, technical business engineering, or software engineering with cloud-native architectures.
• 2 years of experience managing a software engineering, FDE, or a related 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 (e.g., Borderless Lakehouse), Spanner, AlloyDB, Pub/Sub, Dataflow, Kafka).
• Experience building agentic AI systems on enterprise data (e.g., 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 across a broad range of data cloud specializations, including data engineering, data science/machine learning, application development, operations, and investigative 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.

About the job

Responsibilities

• Serve as the technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• Partner with business and tech leadership to define requirements for high-value opportunities, deploying specialized experts to key accounts.
• Lead technical hiring for Forward Deployed Engineer (FDE), evaluating data engineering expertise, systems engineering, and coding skills to build an exceptional engineering team.
• Identify skill gaps in emerging tech (e.g., Model Context Protocol (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.