Staff Data Engineer, Trust and Safety Tech and Data Enablement
Senior data engineer on Google's Trust and Safety data team, architecting AI-ready data pipelines from scratch and integrating fragmented legacy databases into a unified single source of truth to help Google comply with global content regulations. Works with Python, SQL, and big-data tools (Hadoop/Spark), ETL, and ML models, partnering with legal and policy stakeholders.
- Compensation
- $186K–$269K / yr
- Location
- Washington D.C., DC, USA; Atlanta, GA, USA; Austin, TX, USA
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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
Nice to have
- 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
- Interpreting and designing data elements, structures, and relationships; 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)
Full description
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
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.
- 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.
Minimum qualifications:
- Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.