Engineering Manager (Analytics Engineering, Core Data Science)

Join Pinterest as an Analytics Engineering Manager II, Core Data Science. In this role, you will lead a high-performance team of analytics engineers, develop best practices for instrumentation and experimentation, and build AI tools and analysis pipelines. You will work closely with data scientists and engineers to enable self-service of key datasets and insights, and contribute to comprehensive documentation of tools and datasets. You will also collaborate cross-functionally to build and communicate key insights.

- Construire, inspirer et développer une équipe performante d'ingénieurs en analyse, en favorisant une culture inclusive. - Développer des meilleures pratiques pour l'instrumentation et l'expérimentation, et les communiquer aux équipes d'ingénierie produit. - Travailler en étroite collaboration avec les scientifiques des données et les ingénieurs pour construire des outils et des processus qui permettent l'auto-service des ensembles de données clés.

- 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 - Experience conducting workflow orchestration and ETL/ELT with huge, complicated datasets and handling DAG data dependencies - 6+ years of experience working with data in a fast-paced, data-driven environment, including experience managing engineers - High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables - 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 - Bachelor's, Master's, or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline, or equivalent experience - 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) - 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 - Experience in automation, anomaly detection, or infrastructure/workflow optimization - Experience building internal tooling to maximize engineering velocity