When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
As a Customer Engineer (CE) with a specialty in Data Analytics, you will partner with technical sales teams to differentiate Google Cloud to our customers. You will serve as a technical expert responsible for accelerating technical wins and adoption of complex, specialized workloads. You will leverage your expertise in our product areas, in partnership with Platform CEs, to design data foundation architectures and develop MVPs (Minimum Viable Products) to promote new, highly specialized solutions to customers. You will solve analytics-centered customer issues and provide a critical feedback loop to unblock customers and influence product development.
In this role, you will understand their business and technical requirements, and present practical and useful solutions on Google Cloud. You will blend sales expertise, market knowledge and direct technical engagement to show the value of the Google Cloud portfolio.
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.
- Drive the technical win for complex workloads within data analytics to ensure rapid and successful adoption, primarily supporting the business cycle from use-case identification and technical evaluation through customer ramp.
- Combine sales strategies and direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
- Provide technical consultation to customers, acting as a technical advisor and building lasting customer relationships.
- Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market team.
- Provide critical feedback from customer engagements to product and engineering teams to improve architectures and solutions. Work within product and engineering management systems to document, prioritize, and drive resolution of customer feature requests and issues.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 6 years of experience with cloud native architecture in a customer-facing or support role.
- Experience with Big Data technologies or concepts (e.g., analytics warehousing, data processing, data transformation, data governance, data migrations, ETL, ELT, SQL, Spark, performance or scalability optimizations, and batch versus streaming).
- Experience using programming languages to design demos, prototypes, or workshops for customers.
- Experience presenting to technical stakeholders or executive leaders.
- Ability to communicate in Mandarin and English fluently to support client relationship management.
Preferred qualifications:
- Experience with developing data warehousing, data lakes, batch/real-time event processing, data migrations and data governance on cloud native architectures.
- Experience with architecture design, implementing, and query optimization of scalable and distributed systems.
- Experience with open source technology, such as Hadoop/Spark/Flink/Trino/Iceberg.
- Experience with cloud computing (e.g., infrastructure, storage, platforms) and the cloud market, engaged dynamics, and customer buying behavior.
- Experience developing agents using frameworks such as LangGraph, Semantic Kernel, or the Google AI Agent Development Kit (ADK).
- Experience understanding customer requirements with the ability to break down requirements and design technical architectures.