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Open nowFirst seen 3 hours ago

Staff Data Engineer, Trust and Safety Tech and Data Enablement

Google3,445 open roles

Where
Washington D.C., DC, USA; Atlanta, GA, USA; Austin, TX, USA
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Your applicationOpen nowStaff Data Engineer, Trust and Safety Tech and Data EnablementGoogle · Washington D.C., DC, USA; Atlanta, GA, USA; Austin, TX, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

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The clock on this job

Early applications get read.

12.8% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 30 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.8%7 days
  4. 20.6%14 days
  5. 35.6%30 days
  6. 58.4%60 days
This job: first seen 3 hours ago

Google median: 30 days open

The posting

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

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.

****Use this template only when existing Area Templates don't apply. For one-off use cases, not ongoing roles. If you need to create a NEW template for your team submit a request at go/jdreview.****

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.
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