Google
Data Engineer, gUP Engineering
Found: Today
Data Engineer, gUP Engineering
About the job
gTech’s Product and Tools Operations team (gPTO) leverages deep user, operational, and technical insights to innovate Google's Ads products into customer experiences that are so intuitive (or automated) that they require no support at all. gPTO partners closely with gTech’s Support, Professional Services, Product Management, and Engineering teams to innovate and simplify our Ads products and build the productivity tools ecosystem for gTech users. Be a central technical leader in the AI-first transformation across Google!As an L4 Data Engineer in gUP Engineering, you will drive technical direction and own end-to-end data architectures that power support and product experiences across Google's ecosystem. You will solve complex data challenges, steer technical strategy, and collaborate with fellow engineers while pushing Google’s data infrastructure to scale.In gTech Users and Products (gUP), our mission is to advocate for Google’s users by creating helpful and trusted experiences across the product ecosystem. We achieve this by meeting partners and consumers where they are with support and help, representing their needs with our product partners and proposing fixes and features that elevate their engagement with Google's diverse product ecosystem. Additionally we provide a range of product services that ensure our products are optimized for every user, no matter where they are in the world (e.g., localization, digitization, partner integration and more).Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $130000 - $187000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.info_outline
In most instances, this position requires in-person interviews as part of the hiring process.The application window will be open until at least November 26, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.
- 3 years of experience with data engineering, database architecture or distributed systems.
- Experience with data modeling, data warehousing, and building ETL/ELT pipelines using SQL and languages such as Python, Java, or Go.
- Experience working with stakeholders to translate business requirements into technical data architectures.
Preferred qualifications:
- Master’s or PhD degree in Computer Science, Engineering, or a related field.
- Experience integrating AI/Large Language Model (LLMs) or productionizing machine learning models within distributed data pipelines.
- Experience mentoring junior data engineers and establishing architectural standards, test coverage, and documentation best practices across teams.
- Deep subject-matter expertise across Google's data infrastructure and ecosystem (e.g., Flume/Beam, Spanner, F1 Query, PLX, or equivalent cloud big data architectures).
- Excellent written and verbal communication skills, with demonstrated ability aligning and influencing cross-functional technical and executive partners.
- Proven track record of owning and scoping complex, ambiguous technical initiatives from design through production launch.
Responsibilities
- Manage the architecture, technical roadmap, and priorities for large-scale data products, pipelines, and reporting systems within gUP.
- Lead complex architectural constraints across data sources, target interfaces, storage systems, and latency/frequency requirements while building components that scale cleanly.
- Identify difficult, nebulous technical problems and deliver optimal solutions without needing continual oversight or steering.
- Leverage state-of-the-art AI and machine learning techniques to automate workflows and productionize models within large-scale distributed data processing pipelines.
- Partner directly with Users and Products leadership and cross-functional teams to translate strategic business needs into technical data architecture roadmaps.
