US: $130000 - $187000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Manage the architecture, technical roadmap, and priorities for large-scale data products, pipelines, and reporting systems within gUP.
Lead complex architectural constraints across data sources, target interfaces, storage systems, and latency/frequency requirements while building components that scale cleanly.
Identify difficult, nebulous technical problems and deliver optimal solutions without needing continual oversight or steering.
Leverage state-of-the-art AI and machine learning techniques to automate workflows and productionize models within large-scale distributed data processing pipelines.
Partner directly with Users and Products leadership and cross-functional teams to translate strategic business needs into technical data architecture roadmaps.
Minimum qualifications
Bachelor’s degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.
3 years of experience with data engineering, database architecture or distributed systems.
Experience with data modeling, data warehousing, and building ETL/ELT pipelines using SQL and languages such as Python, Java, or Go.
Experience working with stakeholders to translate business requirements into technical data architectures.
Preferred qualifications
Master’s or PhD degree in Computer Science, Engineering, or a related field.
Experience integrating AI/Large Language Model (LLMs) or productionizing machine learning models within distributed data pipelines.
Experience mentoring junior data engineers and establishing architectural standards, test coverage, and documentation best practices across teams.
Deep subject-matter expertise across Google's data infrastructure and ecosystem (e.g., Flume/Beam, Spanner, F1 Query, PLX, or equivalent cloud big data architectures).
Excellent written and verbal communication skills, with demonstrated ability aligning and influencing cross-functional technical and executive partners.
Proven track record of owning and scoping complex, ambiguous technical initiatives from design through production launch.
Bachelor’s degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.
3 years of experience with data engineering, database architecture or distributed systems.
Experience with data modeling, data warehousing, and building ETL/ELT pipelines using SQL and languages such as Python, Java, or Go.
Experience working with stakeholders to translate business requirements into technical data architectures.
