Data Engineer, gUP Engineering
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Data Engineer, gUP Engineering
106,000–151,000 / Year
Location
Boulder, CO, USA
Experience
Mid
Posted
Jul 22, 2026
Apply by
August 21, 2026
Applicants
0
Early applicantEasy applyFull-timeWork from Office
Job Description
info_outline XThe application window will be open until at least August 05, 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. 1 year 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.). 1 year of experience coding in one or more programming languages. Experience working with data models by performing exploratory queries and scripts. Preferred qualifications: Master’s degree in Engineering, Computer Science, or a related technical discipline. 1 year of experience partnering with cross-functional stakeholders and managing project plans to deliver on time, budget, and scope. Experience writing and maintaining scalable ETLs operating across structured and unstructured data sources. Proven experience in large-scale distributed data processing alongside proficiency with Unix and GNU/Linux environments. Expertise designing data models and data warehouses, with strong familiarity in NoSQL and distributed database systems. Strong background modeling real-world business processes, supported by excellent written communication, organizational, and problem-solving skills.
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. Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life. Our teams of trusted advisors support customers globally. Our solutions are rooted in our technical skill, product expertise, and a thorough understanding of our customers’ complex needs. Whether the answer is a bespoke solution to solve a unique problem, or a new tool that can scale across Google, everything we do aims to ensure our customers benefit from the full potential of Google products. To learn more about gTech, check out our video.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $106000 - $151000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google.
Responsibilities Utilize AI technologies to integrate complex data streams directly into scalable, full-stack software applications and operational workflows. Design, develop, and support data pipelines, data warehouses, and automated ETL systems using traditional and distributed data frameworks. Implement critical modifications to existing data models while continuously refining pipelines to resolve core technical and business issues. Partner with data scientists, support engineers, and cross-functional stakeholders to productionize advanced statistical and machine learning models within active data pipelines. Write comprehensive technical design documentation while developing investigative tools to unlock actionable business insights and maintain evolving data architecture.
Key Responsibilities
- Utilize AI technologies to integrate complex data streams into scalable software applications and operational workflows.
- Design, develop, and support data pipelines, data warehouses, and automated ETL systems using traditional and distributed data frameworks.
- Implement critical modifications to existing data models and refine pipelines to resolve technical and business issues.
- Partner with data scientists and cross-functional stakeholders to productionize statistical and machine learning models.
- Write technical design documentation and develop investigative tools to unlock business insights.
Requirements
- Bachelor's degree or equivalent practical experience
Skills Required
Data pipeline designDimensional data modelingFlumeDataFlowSparkProgramming languagesExploratory queriesUnixGNU/LinuxNoSQLDistributed database systemsStructured data sourcesUnstructured data sourcesWritten communicationOrganizational skillsProblem-solving skills
Benefits
- 15% bonus target
- Equity
- Benefits
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