GCP Data Engineer (Google)

This role is responsible for building and scaling the massive, reliable data infrastructure that powers critical decisions, product features, and advanced analytics across our organization—impacting billions of users daily. As a Data Engineer, you will be a core architect and builder of our next-generation data pipelines, transforming raw, high-volume data into structured, actionable insights. You will design, construct, implement, and rigorously manage end-to-end data processing systems, focusing on robustness, efficiency, security, and high throughput. Your challenge will be to translate complex, ambiguous business requirements from product managers and data scientists into scalable, fault-tolerant solutions using cutting-edge Google Cloud Platform (GCP) technologies. This includes deep involvement with services like BigQuery, Dataflow, Cloud Composer, and Pub/Sub. Your contributions will be instrumental in enabling data-driven optimization across areas such as product development, machine learning model training, and operational efficiency, making you a vital partner in Google's ongoing innovation. We're looking for an engineer ready to tackle petabyte-scale data challenges and champion data quality across the enterprise. Responsibilities • Design and Build: Develop, test, and maintain scalable data pipelines (ETL/ELT) using technologies like Dataflow (Apache Beam), Composer (Apache Airflow), and BigQuery. • Infrastructure Management: Architect and optimize data models within BigQuery for performance and cost efficiency, supporting high-volume analytical workloads. • Collaboration: Work closely with Data Scientists, Analysts, and Software Engineers to understand data needs and ensure data quality, integrity, and security across all platforms. • Operational Excellence: Monitor and troubleshoot production data systems, implementing best practices for continuous integration and deployment (CI/CD) of data pipelines. • Innovation: Explore and recommend new data technologies and cloud services to enhance data architecture and governance.