Principal Data Quality Engineer for AI Data

Google Inc.; San Jose (CA); On-site; USD 307,000 - 427,000; Full time

Job summary

Google San Jose is seeking a Principal Engineer, Data Quality for the AI Foundry Data Quality team. You will lead technical direction, orchestrate upstream capabilities, and deliver high-quality datasets for Search and DeepMind training at Google scale.

You will influence data quality across large, dynamic systems and collaborate with researchers and engineers to drive data-driven ML and Search features. The role emphasizes architecture, metrics, and scalable data plumbing.

Qualifications

• Bachelor’s degree in CS/Engineering/Mathematics/Statistics/Economics, or equivalent practical experience.
• 15 years of software engineering experience, building and operating large-scale systems.
• 7 years in a technical leadership role (with or without direct reports).
• 5 years developing and deploying ML models on data sets.

Responsibilities

• Lead the strategy, technical vision, and roadmaps for the data quality team to deliver high-quality datasets.
• Drive the architecture toward data flywheel for ML training data and Search Context that can self-correct and self-heal.
• Collaborate with DeepMind and Search researchers, data analysts, and engineers to identify VoD signals.
• Partner with infrastructure owners to evolve crawl, processing, and signal enrichment for high-quality datasets.
• Provide technical guidance to components of the context quality area, including large-scale signal developments and metrics.

Skills

Software engineering experience

Technical leadership

ML model development

Data systems experience

Education

Bachelor's degree in CS/Engineering/Math/Stats/Economics or equivalent

Job description

Google San Jose is seeking a Principal Engineer, Data Quality for the AI Foundry Data Quality team. You will lead technical direction, orchestrate upstream capabilities, and deliver high-quality datasets for Search and DeepMind training at Google scale.

You will influence data quality across large, dynamic systems and collaborate with researchers and engineers to drive data-driven ML and Search features. The role emphasizes architecture, metrics, and scalable data plumbing.