Staff Data Platform Engineer - AI-Driven Analytics

Pinterest; Palo Alto (CA); Hybrid; USD 177,000 - 365,000; Full time

Job summary

Pinterest is seeking a Staff Software Engineer to lead design and execution of scalable data warehouse and analytics platforms. You will shape architecture, mentor engineers, and drive cross‑functional initiatives that accelerate data-driven decision making across core teams.

You will collaborate with Product, Data Science, and Engineering to translate needs into robust data products, while guiding architectural decisions and ensuring reliable, scalable delivery.

Qualifications

• Bachelor’s degree in CS/Engineering or equivalent experience.
• 8+ years of experience with large-scale data warehouses.
• 5+ years building data warehouses and tools (Spark, Trino, Flink, Airflow, etc.).
• Ability to work with cross-functional partners across orgs.
• Hands-on experience with AI coding tools (Cursor, Claude Code, Codex).
• Experience building AI tools to accelerate data pipeline analytics.

Responsibilities

• Define and design Pinterest's data warehouse architecture, storage, and access patterns.
• Lead ambiguous, cross‑functional initiatives in data warehouse and analytics domains.
• Mentor engineers and set technical direction for complex design and trade-offs.
• Collaborate with Product, Data Science, and Engineering to align needs and progress.

Skills

Cross-functional collaboration

Leadership

AI tool familiarity

Data-centric mindset

Education

Bachelor’s degree in Computer Science/Engineering or related field

Equivalent experience

Tools

Spark

Trino

Flink

Airflow

Querybook

Superset

DataHub

Job description

Pinterest is seeking a Staff Software Engineer to lead design and execution of scalable data warehouse and analytics platforms. You will shape architecture, mentor engineers, and drive cross‑functional initiatives that accelerate data-driven decision making across core teams.

You will collaborate with Product, Data Science, and Engineering to translate needs into robust data products, while guiding architectural decisions and ensuring reliable, scalable delivery.