Sr. Staff Software Engineer, Data Product Platform

Pinterest Pinterest · Consumer · San Francisco, CA · Data Engineering

This role is for a Sr. Staff Software Engineer on the Data Product Platform team at Pinterest. The primary focus is on architecting and driving solutions for data warehouses, analytical tools, and data governance. A key aspect involves building AI-assisted data analytics capabilities and platforms to accelerate data pipeline authoring and engineering productivity, leveraging AI coding tools. The role is engineering-focused within the enterprise AI domain, aiming to build foundational AI analytics products.

What you'd actually do

  1. Spearhead the definition and design of Pinterest's data warehouse architecture, storage, governance, and usage.
  2. Set the strategic direction for world-class data analytics tools, including experimentation platforms, ad hoc analysis systems, data visualization, data pipeline authoring, and AI-assisted data analytics capabilities.
  3. Define, design, and drive the adoption of comprehensive data governance policies and tooling to ensure data quality, promote responsible data handling, and maximize efficiency.
  4. Lead ambiguous, highly challenging, and cross-functional initiatives across the data ecosystem, expertly managing trade-offs among unique requirements and constraints (e.g., usability, efficiency, and compliance) to deliver a cohesive user experience.
  5. Drive measurable adoption and significant business impact across the entire product portfolio through platform and tooling initiatives.

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
  • 12+ years of relevant industry experience with large scale data warehouses, data tools and platforms.
  • 5+ years experience building data warehouses and tools around the data ecosystem with technologies such as Spark, Trino, Flink, Airflow, Querybook, Superset, DataHub, etc.
  • Ability to work with cross-functional partners across multiple organizations.
  • Hands-on experience building tools and data pipelines leveraging AI coding tools, e.g. Cursor, Claude Code, Codex, etc.
  • Hands-on experience building AI tools and platforms to accelerate data pipeline authoring, data analytics and engineering productivity.

What the JD emphasized

  • AI-assisted data analytics capabilities
  • building the foundation for our next generation of AI analytics products
  • Hands-on experience building tools and data pipelines leveraging AI coding tools
  • Hands-on experience building AI tools and platforms to accelerate data pipeline authoring, data analytics and engineering productivity

Other signals

  • AI-assisted data analytics capabilities
  • building the foundation for our next generation of AI analytics products
  • Hands-on experience building tools and data pipelines leveraging AI coding tools
  • Hands-on experience building AI tools and platforms to accelerate data pipeline authoring, data analytics and engineering productivity