Staff Software Engineer (Analytics Engineering, Core Data Science)

Join Pinterest as a Staff Software Engineer in Analytics Engineering and Core Data Science. You will lead a high-performance team, develop best practices for instrumentation and experimentation, and build AI tools and analysis pipelines. You will work closely with Data Scientists and Engineers, contribute to documentation, and collaborate with cross-functional teams. The ideal candidate has 6+ years of experience in a data-driven environment, fluency in SQL and a scripting language, and a strong background in using AI to improve workflow.

- Lead and mentor a diverse team of analytics engineers, fostering an inclusive culture and developing best practices for instrumentation and experimentation. - Collaborate closely with Data Scientists and Engineers to build tools and processes that enable self-service of key datasets, insights, and metric investigations. - Utilize AI to accelerate analysis, prototyping, and iteration, while applying judgment and verification to ensure correctness and quality.

- High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables - 6+ years of experience working with data in a fast-paced, data-driven environment - 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 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 - 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) - Experience building internal tooling to maximize engineering velocity - Experience in automation, anomaly detection, or infrastructure/workflow optimization