Scaled Insights Analyst, Intelligence and Scaled Insights, YouTube at Google in New York, NY, USA

Job description

• YouTube's Intelligence and Scaled Insights function dissects content risks across social media to support decision-making, ensuring YouTube is a safe place for its billions of users. Resourceful yet technical, this team's work runs the gamut from scaled and strategic to detailed and in-depth. Our Scaled Insights pillar leverages AI to build technology, tools, and scaled capabilities in support of our larger team and the rest of YouTube. Our Intelligence Desk, which Scaled Insights supports, identifies and investigates content risks to ultimately drive mitigations for the benefit of our users. As part of our team, you will be a key contributor toward building an exceptional intelligence function, enjoying a career at the intersection of technology, online safety, and real-world impact.
• As a scaled insights analyst, you will work with analysts on YouTube Intelligence Desk and other teams across YouTube to translate information needs into tangible technical pathways that ultimately satisfy those information needs. You will leverage Google's suite of AI and technical capabilities to build bespoke capabilities that identify trends and glean difficult to acquire information in support of YouTube's mission, with an emphasis on Trust and Safety and keeping our users safe.
• At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.
• Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
• US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits
• Learn more about benefits at Google.

Responsibilities

• Design, build, and optimize robust, production-grade data architecture and ETL pipelines. Serve as the technical steward for the team's data platforms, ensuring maximum data reliability, integrity, and scalable governance across complex datasets.
• Partner closely with stakeholders to translate ambiguous product and threat-intelligence analytical needs into structured data models, pipelines, and self-serve services.
• Design, implement, and evaluate intelligent, agentic analytics systems leveraging Large Language Models (LLMs) to automate deep insight generation, anomaly detection, and operational workflows.
• Architect and maintain internal tools, frameworks, and foundational tables that enable self-serve analytics, reducing data friction and dependencies for non-technical stakeholders. Streamline complex SQL queries and scale value-creating data integrations into automated, repeatable workflows.
• Own ambiguous, non-routine technical challenges end-to-end. Identify latent infrastructure vulnerabilities, process messy unstructured data volumes, and apply advanced data modeling frameworks with minimal supervision.

Requirements

• Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• 7 years of experience coding in Python and SQL.
• 7 years of experience designing, deploying, and managing data pipelines, including schema management, data modeling, exploratory querying, and data workflows.
• Experience with data warehousing or data modeling concepts.
• Experience with big data technologies (e.g., Hadoop, Spark, Python, or R).
• Experience with translating business requirements to BI solutions.
• Experience with explaining technical analysis to stakeholders.

Skills

Required

• Python
• SQL
• Data pipeline design and management
• Schema management
• Data modeling
• Exploratory querying
• Data workflows

Preferred

• Data warehousing