Staff Data Engineer, Trust and Safety Tech and Data Enablement

Google•Washington, DC

9d•$186,000 - $269,000

About The Position

The Google Cloud Consulting Professional Services team guides customers through important stages of their cloud journeys, helping businesses transform through Google’s network, data centers, and software infrastructure. As a Staff Data Engineer supporting the Legal Content Policy and Standards (LCPS) team, you will lead the design and development of foundational, AI-ready data infrastructure to help Google comply with global content regulations and protect user safety. You will architect scalable data pipelines from scratch and integrate fragmented legacy systems, including isolated transactional MySQL databases, into a modern data warehouse and unified reporting source of truth. You will serve as a technical advisor to policy leads, legal experts, and other non-technical stakeholders, translating ambiguous regulatory requirements into structured data models and automated workflows.

Requirements

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 such as Python, R, and SQL for datasets.
10 years of experience with data processing software such as Hadoop, Spark, Pig, and Hive, and algorithms such as MapReduce and Flume.
Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.

Nice To Haves

10 years of experience designing robust, scalable, synchronized pipelines and dimensional models.
Experience partnering with non-technical stakeholders to translate ambiguous business or regulatory requirements into structured technical designs.
Experience interpreting and designing data elements, structures, and relationships to deliver consistent and predictable data models.
Experience designing and implementing complex ETL pipelines for synchronous and asynchronous system integration.
Experience designing, evaluating, and managing scalable data storage and processing ecosystems such as Data Lakes, Data Warehouses, and Data Mesh.

Responsibilities

Guide consultations with cross-organizational partners to define data sources and validation standards, and partner with engineers and data scientists on ML training.
Oversee development of infrastructure and business solutions that scale common frameworks; design secure data processing systems and advocate for best practices.
Direct development and scaling of data models, pipelines, and exchange formats to reduce data fragmentation and improve 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.
Design and develop foundational, AI-ready data infrastructure for regulatory compliance and user safety.
Architect production-level data pipelines and integrate fragmented legacy databases into a modern data warehouse and unified reporting source of truth.
Partner with policy leads, legal experts, and other non-technical stakeholders to translate regulatory requirements into data models and automated workflows.