Customer Engineering Manager, Data Analytics, FSI, Google Cloud

Google•Atlanta, GA

23h•$192,000 - $267,000

About The Position

As a Customer Engineering (CE) Manager, you will lead and scale a high-performing team of technical subject matter experts and solutions architects responsible for partnering with customers to architect, validate, and deploy modern data platforms. You will guide your team in providing trusted technical advisory, accelerating complex workload migrations, eliminating technical friction, and turning architectural direction into measurable business outcomes. In this role, you will advocate Google Cloud’s Agentic Data Cloud strategy, positioning our unified data suite—including BigQuery, Knowledge Catalog, and the Borderless Lakehouse—as the foundational platform powering enterprise-grade, autonomous agentic workflows. You will lead your team to demonstrate how robust data governance, semantic cataloging, and unified lakehouse architectures directly eliminate high-quality, real-time AI agents. You will foster a culture of deep technical ownership, engineering excellence, and customer empathy. By mastering the mechanics of architecture, delivery, and consumption across Google Cloud’s data and AI portfolio, you will partner closely with Sales leadership and building strong ecosystems of customer relationships and cross-functional partnerships to shape customer transformation roadmaps, multiply organizational impact, and establish Google Cloud as the definitive foundation for the Agentic AI era. 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Requirements

Bachelor's degree or equivalent practical experience.
10 years of experience with cloud native architecture in a customer-facing role.
5 years of Experience in pre-sales or field engineering leadership roles at an enterprise technology company, or similar customer-facing experience.
Experience working in the Financial Services Industry or with Financial Services customers
Experience pitching a connected story of modern Data & AI foundations to C-level executives (CDOs, CAIOs, CTOs, CIOs, LoBs) for Financial Services use cases with clear business outcomes

Nice To Haves

Experience as a thought leader in Data Analytics and AI, specialized in technical sales and strategies around holistic customer and industry solutions.
Experience in technical sales or professional consulting in the fields of cloud computing, data analytics, information lifecycle management, and Big Data.
Experience in people management leading technical sales or consulting teams for Cloud Computing or Data Analytics products and platforms.
Experience influencing cross-functional teams, customers, and partners to impact business goals, customer experience, and customer expansion.

Responsibilities

Lead a team of Data Analytics technical experts in a matrixed organization. Focus on talent strategy, assessing go-to-market readiness and gaps in CE preparedness, skills development.
Lead the broader region's Data Analytics technical community, to ensure sub-regional technical contributions, achieve scale through artifact and innovation sharing.
Influence cross-functional teams, including Product Management and Engineering, lead and own high impact customer C-level or Information Technology Decision Maker (ITDM) relationships who make or influence Data and AI enterprise technology decisions.
Lead the team to work with Google Cloud Platform to demonstrate and prototype our Agentic Data Cloud and AI products or integrations in customer/partner environments.
Coach team to guide enterprise customers in leveraging industry-specific unified data foundations to power real-time, AI-driven experiences, with strategic and technical differentiated solutions that address technical bottlenecks.