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.

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.

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.

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.