Data Engineer, Data Architecture and Engineering, gTech Strategy and Operations

As a Data Engineer in gData, you’ll design and build the foundational data infrastructure that powers the GBO data needs. You’ll manage complex, large-scale issues using Google’s proprietary tech stack to deliver a highly reliable Single Source of Truth. In this role, you will have the opportunity to architect innovative data pipelines and solutions that directly enable predictive problem-solving and AI-driven business decisions at scale.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.

Responsibilities:

• Design, develop, test, and maintain reliable and scalable data pipelines and Extract, Transform, Load and Extract, Load, Transform (ETL/ELT) architectures using Google's distributed data systems (e.g., advanced SQL, Python).
  
• Contribute to the modernization of the Google Ads Data Infrastructure (GDI) and Customer Data Platform (CDP), optimizing data models to ensure our Single Source of Truth remains robust and performant.
  
• Partner closely with cross-functional stakeholders across gTech and Customer Engagement (CE) to translate evolving business requirements into actionable technical data solutions.
  
• Work seamlessly with Data Scientists and Business Analysts to transition analytical prototypes, metrics, and models into stable, production-grade reporting environments.
  
• Advocate data quality by authoring clear technical design documents, executing code reviews, and proactively resolving complex bugs and support escalations.
  

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
• 1 year of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume).
  
• Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.
• Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript.
  

Preferred qualifications:

• Master's degree or other advanced degree in Computer Science or related technical field or equivalent practical experience.
• Experience in technical consulting and working with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.
• Experience working with Big Data, information retrieval, data mining, or machine learning.
  
• Experience in building multi-tier high availability applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, TensorFlow).
  
• Experience in a customer-facing or customer service role, with practice using AI technologies to augment, improve, or automate the development process.