As the Manager, Data Engineering on the DAS team, you will lead a team of data engineers designing and building the next generation of our data infrastructure.
This role needs a strong technical leader who can manage and inspire high-performing data engineers while staying close enough to the work to guide architecture decisions on massive datasets. You will be a partner to functional teams, turning business needs into prioritized solutions. You will also set the standards and best practices that raise the bar across the organization.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $186000 - $269000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Define the technical strategy for the centralized data warehouse, including scalable pipelines, ETL/ELT processes, and AI-ready data foundations.
- Oversee performant data modeling and SQL transformation standards, guiding architecture and optimization to support analytics at scale.
- Drive organization-wide data quality, governance, and operational reliability, holding the team accountable for trusted data assets.
- Partner with executive leaders, data scientists, and engineering teams to translate complex business needs into prioritized roadmaps.
- Manage resources and capacity to deliver projects on time while promoting best practices like code and design reviews.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
- 5 years of experience coding in one or more programming languages, designing data pipelines, and experience with data warehousing concepts, data modeling techniques, and scalable architecture patterns.
- 3 years of experience in a people management, supervision, or team leadership role.
Preferred qualifications:
- Experience with GCP data services (BigQuery) or other cloud platforms data services, AI infrastructure (Vertex AI), and making build-versus-buy decisions.
- Experience driving AI data strategy, including Generative AI, LLM integration, RAG, and vector databases.
- Experience setting up data quality, governance, reliability frameworks, and production-grade pipeline engineering standards.
- Experience setting technical roadmaps, managing priorities, and delivering projects across teams.
- Experience or familiarity with consumer electronics, or supply chain data.
- Executive communication skills to influence leaders, and building high-performing teams.