Staff Data Engineer, Trust and Safety Tech and Data Enablement

info_outline X Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Washington D.C., DC, USA; Atlanta, GA, USA; Austin, TX, USA. Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
• 10 years of experience writing code using specialized languages and libraries (e.g., Python, R, SQL) for datasets.
• 10 years of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume).
• Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.

Preferred qualifications:

• 10 years of experience in designing robust, scalable, and synchronized pipelines, as well as dimensional modeling.
• Experience partnering with non-technical stakeholders to translate ambiguous business or regulatory requirements into structured technical designs targeting the preferred qualification of partnering with non-technical stakeholders
• Interpreting and designing data elements, structures, and relationships to deliver consistent and predictable data models. Designing and implementing complex ETL pipelines for synchronous and asynchronous system integration.
• Designing, evaluating, and managing scalable data storage and processing ecosystems (e.g., Data Lakes, Data Warehouses, Data Mesh).

About the job As a Staff Data Engineer supporting the Legal Content Policy and Standards (LCPS) team, you will lead the design and development of Google's foundational, AI-ready data infrastructure to help Google comply with global content regulations and protect user safety. In this senior-level role, you will architect complex, scalable data pipelines from scratch and seamlessly integrate fragmented legacy systems into a unified Single Source of Truth based on Experience designing and building complex, production-level pipelines from scratch and integrate highly fragmented legacy databases (such as isolated transactional MySQL databases) into a modern data warehouse to build a unified reporting 'single source of truth'. You will act as a trusted technical advisor, partnering closely with non-technical stakeholders-including policy leads and legal experts-to translate ambiguous regulatory requirements into robust data models and automated workflows by partnering with non-technical stakeholders (policy leads, legal specialists) and translate highly ambiguous regulatory requirements into a structured database schema. 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

• Guide consultations with cross-organizational partners to define data sources and validation standards, partnering with engineers and data scientists on ML training.
• Oversee the development of infrastructure and business solutions to scale common frameworks, and design secure data processing systems while advocating for best practices.
• Direct the development and scaling of data models, pipelines, and exchange formats to reduce data fragmentation and increase data discoverability.
• Leverage , deploy, and continuously train ML models while implementing new storage and serving systems to scale across Data Engineering.
• Manage data profiling and analysis using existing data models and custom infrastructure to clarify client requirements and enable data-driven decision-making.