
Staff Data Engineer, Trust and Safety Tech and Data Enablement
Washington, DCJob$186–269K/yrPosted 4 days agoStill listed today
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Job overview
Google’s Staff Data Engineer will support the Legal Content Policy and Standards team by building AI-ready data infrastructure for regulatory compliance and user safety. The role leads complex data pipeline design, integrates fragmented legacy systems into a unified source of truth, and advises policy and legal stakeholders on translating regulatory requirements into data models and automated workflows.
Skills & qualifications
Skills
Qualifications
Full job description
Staff Data Engineer, Trust and Safety Tech and Data Enablement Share Staff Data Engineer, Trust and Safety Tech and Data Enablement
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corporate_fareGoogleplaceWashington D.C., DC, USA; Atlanta, GA, USA; +2 more; +1 more Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.
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info_outline XNote: 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:
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Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
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10 years of experience writing code using specialized languages and libraries (e.g., Python, R, SQL) for datasets.
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10 years of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume).
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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 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. 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.
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire. If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form. Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes. Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.
How this job compares
Set against the 6,038 open Data Engineer jobs on Olive Jobs, counted November 5, 2026.
- Pay
- This posting$186–269K/yr
- US median$125–208K/yr4,707 salary ranges
This range's midpoint, $228K, is 37% above the US median midpoint ($166K) — in the top quarter of Data Engineer salary ranges across the US.
- Openings
- Olive Jobs lists 6,038 open Data Engineer jobs across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 10 years of experience — more than three in four Data Engineer postings that state a minimum ask for (median 5 years, across 5,295 postings).
- Education
- 2,348 of the 6,038 open Data Engineer jobs (39%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 5, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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