Software Engineer II, Data Analytics & Engineering

Pinterest•San Francisco, CA

3d•$123,696 - $254,667•Hybrid

About The Position

We’re looking for a Software Engineer II, Data Analytics and Engineering to improve the quality, reliability and velocity of data science and product development at Pinterest. You’ll build scalable data foundations, analytics tooling and analysis pipelines that enable trusted, self-service access to datasets, insights and metric investigations across cross-functional teams.

Requirements

Minimum of 2 years of experience delivering analytics engineering or data solutions in a fast-paced, data-driven environment.
Experience using SQL and Python, R or a comparable programming language to work with large, high-dimensional datasets, including nested data structures, window functions, query optimization and data partitioning.
Experience building and operating data workflows, including workflow orchestration, ETL/ELT pipelines and DAG dependencies across complex datasets.
Experience translating open-ended partner needs into clear, impactful technical objectives and collaborating across Product, Engineering, Data Science, Data Engineering and Business Intelligence teams.
Demonstrated ability to use AI to improve speed and quality in day-to-day engineering workflows.
Strong track record of critically evaluating and verifying AI-assisted work through testing, data validation, source-checking or 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 degree in a relevant field such as Computer Science, Statistics, Mathematics or a related discipline, or equivalent experience.

Responsibilities

Develop and document practical instrumentation and experimentation standards, then partner with product engineering teams to apply them to priority product development work.
Build and improve scalable analysis pipelines and tooling that produce reliable insights at scale and strengthen understanding of key data structures and metrics.
Create tools and processes that enable Data Scientists and Engineers to independently access trusted datasets, insights and metric definitions.
Identify data quality and discoverability gaps, advocate for targeted improvements and contribute to reliable, well-governed data practices.
Maintain clear documentation for tools, datasets, metrics and operating practices to make team data assets easier to use.
Partner with Product, Engineering, Data Science, Data Engineering and Business Intelligence teams to communicate actionable insights and inform product improvements.
Use AI to accelerate analysis, prototyping and iteration, while applying judgment and verification to ensure correctness, quality and responsible use.