Join a dynamic Data and Analytics Services (DAS) team focused on empowering the Platforms and Devices (P&D) organization with high-quality, timely data. Our mission is to build and manage a central data warehouse, develop scalable analytics solutions on Google Cloud Platform, and partner with stakeholders to translate complex data needs into actionable insights. We are committed to maintaining data quality and governance to ensure data is a valuable asset for driving product strategy and operational efficiency.

This role offers an exciting opportunity to shape the future of our data infrastructure. You will be instrumental in designing, building, and optimizing robust data pipelines and owning key components of our data warehouse. Leverage your expertise to manage massive datasets, write efficient SQL and Python code, and collaborate with executive leadership and fellow engineers. Contribute to building innovative data foundations and AI-driven insights, while also defining team standards and best practices to advance data quality and AI readiness.

Lead the architecture, development, and ongoing maintenance of data pipelines and ETL/ELT processes for our central data warehouse.

Optimize SQL queries for complex data transformations, analytics, and reporting needs.

Develop and manage data foundations and models that specifically support Artificial Intelligence/Machine Learning (AI/ML) initiatives and generate AI-driven insights. Maintain the underlying data infrastructure.

Collaborate closely with executive business stakeholders, data scientists, and AI teams to understand requirements and design effective data solutions.

Partner with other data engineers to deliver high-quality data solutions and foster technical growth within the team.

We are seeking candidates with a Bachelor’s degree in Computer Science, Engineering, Information Systems, a related quantitative field, or equivalent practical experience.

Possess at least 3 years of dedicated experience in Data Engineering, Data Infrastructure, or Data Analytics roles.

Demonstrate proficiency in data engineering practices and software development using Python or SQL.

Proven experience in managing and maintaining data projects from inception through to production deployment.

Experience in building and maintaining data pipelines specifically designed for Machine Learning, Artificial Intelligence, or advanced analytics workloads is essential.