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X The application window will be open until at least October 12, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
- 5 years of experience coding in one or more programming languages.
- 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
Preferred qualifications: - Master's degree or PhD in Engineering, Computer Science, or a related field.
- Experience integrating AI/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.
- Track record of independently owning and scoping complex, ambiguous technical initiatives from design through production launch.
- Excellent written and verbal communication skills, with demonstrated ability to align and influence cross-functional technical and executive partners.
About the jobgTech'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.
As a 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 scope nebulous, complex data challenges, guide technical strategy, and mentor fellow engineers while pushing Google's data infrastructure to scale.
Data Engineers in gUP lead and deliver on designing and scaling workflows to support critical user journeys. They translate complex business and operational problems into robust, highly scalable technical data models and systems visions. In this role, you will lead telemetry and logging instrumentation architectures at scale to generate actionable product and operational insights, as well as integrating AI/ML workflows and modern analytical frameworks into full-stack data solutions. You will also partner with engineering, product, and data science leaders across Google to align technical roadmaps and solve ambiguous problems at 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: $156000 - $226000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Own the architecture, technical roadmap, and priorities for large-scale data products, pipelines, and reporting systems within gUP.
- Scope difficult, nebulous technical problems and deliver robust, optimal solutions without needing continual oversight or steering.
- Lead complex architectural constraints across data sources, target interfaces, storage systems, and latency/frequency requirements while building components that scale cleanly.
- Leverage 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.
