Senior Data Engineer, Product Analytics, Payments

As a Technical Solutions Consultant, you will be responsible for the technical relationship of our largest advertising clients and/or product partners. You will lead cross-functional teams in Engineering, Sales and Product Management to leverage emerging technologies for our external clients/partners. From concept design and testing to data analysis and support, you will oversee the technical execution and business operations of Google's online advertising platforms and/or product partnerships.

You will be able to balance business and partner needs with technical constraints, develop innovative, cutting edge solutions and act as a partner and consultant to those you are working with. You will also be able to build tools and automate products, oversee the technical execution and business operations of Google's partnerships, as well as develop product strategy and prioritize projects and resources.

Whether it is paying online with Autofill, using tap and pay in stores, or using the Google Pay app, the Payments team at Google is focused on making payments simple, seamless, and secure. In addition to consumer payment technologies, the Payments team also powers the money movement between Google and its consumers and businesses.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience coding in Python and SQL.
• 5 years of experience designing and deploying data pipelines, including managing data schemas and processing data volume workflows.
• 5 years of experience working with machine learning operations and data architecture design.

Preferred qualifications:

• 5 years of experience designing enterprise-scale data platforms and analytics infrastructure.
• 5 years of experience with AI/LLM-based systems, agentic workflows, or intelligent data applications.
• 5 years of experience with using Gemini Command-line Interface, Cider Agents, LLM Extensions, Agent Development Kit (ADK) Agents etc.
• 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders or customers.
• Experience with Machine Learning for production workflows.
• Ability to operate across ambiguity and influence cross-functional technical decisions.