Staff Software, Engineering – Analytics Engineering, Core Data Science

We are looking for a Staff Software Engineer to help improve the quality and velocity of data science at Pinterest. You will shape the future of the people-facing and business-facing products we build by creating rock-solid data foundations and tooling, and by leading a high-performance team that partners closely with our Core Data Science organization.

What you'll do:

• Lead by example, and mentor a 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.

What we're looking for:

• 6+ years of experience working with data in a fast-paced, data-driven environment.
• 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.
• Desired but not required:
  • Experience in automation, anomaly detection, or infrastructure/workflow optimization.
  • Experience building internal tooling to maximize engineering velocity.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times/month and therefore can be situated anywhere in the country.

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