Manager II, Engineering- Analytics Engineering, Core Data Science

Pinterest is hiring an Analytics Engineering Manager II to improve the quality and velocity of data science by building strong data foundations and tooling. The role leads a high-performance team and partners with Core Data Science and cross-functional teams on analytics, instrumentation, experimentation, and data quality.

Responsibilities:

• Build, inspire, and grow a high-performance, diverse team of analytics engineers with an inclusive culture
• Develop best practices for instrumentation and experimentation and communicate those to product engineering teams
• Build and prototype AI tools and analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed
• Use AI to accelerate analysis, prototyping, and iteration—drafting pipelines, exploring approaches, and automating repeatable documentation and QA—while applying judgment and verification to ensure correctness and quality
• Work closely with Data Scientists and Engineers to build tools and processes that enable self-service of key datasets, insights, and metric investigations
• Contribute to comprehensive documentation of tools and datasets, and drive the strategy for better data quality and data democratization within Pinterest
• Work cross-functionally to build and communicate key insights, collaborating with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

Requirements:

• 6+ years of experience working with data in a fast-paced, data-driven environment, including experience managing engineers
• Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R), including nested data structure manipulation, windowing functions, query optimization, and data partitioning techniques
• Experience conducting workflow orchestration and ETL/ELT with huge, complicated datasets and handling DAG data dependencies
• Consistent track record of supplying analytics solutions to existing teams, with the ability to take open-ended goals and scope them into defined, impactful objectives
• A team player able to partner with cross-functional leadership to quickly turn insights into actions, and an accountable operator who proactively identifies project opportunities amid competing priorities
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow, with a strong track record of critical evaluation and verification of AI-assisted work (testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
• Bachelor's, Master's, or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline, or equivalent experience
• Experience in automation, anomaly detection, or infrastructure/workflow optimization
• Experience building internal tooling to maximize engineering velocity