Join our gData team as a Data Engineer to build and design the core data infrastructure essential for GBO data needs. You will tackle complex, large-scale challenges utilizing Google's proprietary technology to establish a highly reliable Single Source of Truth. This role offers the chance to architect innovative data pipelines and solutions that enable scalable, AI-driven business decisions and predictive problem-solving.

Google is dedicated to creating products and services that positively impact the world. Our gTech organization plays a vital role in bringing these innovations to life by providing global customer support. Our team combines technical expertise, deep product knowledge, and a thorough understanding of customer requirements to deliver tailored solutions and scalable tools that maximize the potential of Google products for our clients.

Design, develop, test, and maintain robust and scalable data pipelines, including Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) architectures, leveraging Google's distributed data systems and languages like advanced SQL and Python.

Play a key role in modernizing the Google Ads Data Infrastructure (GDI) and Customer Data Platform (CDP), focusing on optimizing data models to ensure our Single Source of Truth remains both performant and reliable.

Collaborate effectively with cross-functional stakeholders across gTech and Customer Engagement (CE) to translate dynamic business requirements into practical and actionable technical data solutions.

Work closely with Data Scientists and Business Analysts to seamlessly transition analytical prototypes, metrics, and models into stable, production-ready reporting environments.

Champion data quality through the creation of clear technical design documents, conducting thorough code reviews, and proactively addressing complex bugs and support escalations.

A Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience is required.

Possess at least 1 year of experience with data processing software such as Hadoop, Spark, Pig, or Hive, along with expertise in algorithms like MapReduce and Flume.

Demonstrated experience in managing client-facing projects, effectively troubleshooting technical issues, and collaborating with Engineering and Sales Services teams.

Experience in database administration techniques or data engineering is essential, complemented by proficiency in software development using Java, C++, Python, Go, or JavaScript.

Preferred qualifications include a Master's degree or advanced degree in Computer Science or a related technical field, or equivalent practical experience.

Experience in technical consulting and working with data warehouses, including understanding data warehouse technical architectures, infrastructure components, ETL/ELT processes, and reporting/analytic tools, is highly desirable.

Familiarity with Big Data concepts, information retrieval, data mining, or machine learning is a plus.

Experience building multi-tier high availability applications using modern web technologies like NoSQL, MongoDB, SparkML, or TensorFlow.

Customer-facing or customer service experience, particularly using AI technologies to enhance, improve, or automate development processes.