Forward Deployed Engineer, Data Cloud

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 7 years of experience troubleshooting technical issues for internal/external partners or customers.
• Experience with prototyping and customer engagement.

Preferred qualifications:

• 8 years of experience in software engineering, solution architecture, or technical consulting.
• Experience coding in one or more languages (e.g., Python, Java, Go) with a focus on data processing or machine learning applications.
• Experience designing and deploying cloud-native distributed systems, data pipelines, or AI/ML workflows in an enterprise environment.
• Experience in a customer-facing technical role (e.g., Forward Deployed Engineer, Solutions Architect, Sales Engineer) leading technical engagements.
• Experience with SQL and modern data warehousing concepts.

About the job

As a part of the Data and AI Strategic Pursuits ("Forward Deployed Engineering") Team, you will accelerate Product Market Fit for unified Data Cloud and AI capabilities. In this role, you will bridge the gap between platform primitives and enterprises solutions by operating as a "customer-zero" laboratory. You will embed with lighthouse customers using a "Co-Invest, Build, Codify" approach to develop breakthrough solutions in real-world environments, then translate these engagements into scalable product prototypes and repeatable go-to-market blueprints.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Architect integrations that stitch together Google’s AI primitives into functioning, high-value enterprise solutions (e.g., Agentic Workflows, Semantic Data Layers) working to write code and design solutions.
• Resolve ambiguous technical hurdles that prevent adoption by debugging integration issues between disparate systems, optimizing inference latency, and architecting security, governance layers that transform demos into production-ready asset.
• Serve as the primary source of technical truth for Core Engineering by systematically documenting product gaps, API friction, and feature requests discovered during deployment, directly influencing the roadmaps of BigQuery, Vertex, and Looker engineering teams.
• Mentor the builder engineers by establishing coding standards, reviewing architectural designs, and ensuring the team delivers high-quality, scalable, and secure software.
• Validate technical feasibility with customer CTOs and Chief Data Officers by partnering to align proposed architectures with their existing enterprise stacks.