Software Engineer II, Data Analytics & Engineering
About this role
Pinterest is hiring a Software Engineer II to build scalable data foundations and analytics tooling that enable self-service data access and insights across teams. You'll develop instrumentation standards, create analysis pipelines, and partner with cross-functional teams to strengthen data reliability and quality while leveraging AI to accelerate your work.
What you'll do
- Develop instrumentation and experimentation standards in collaboration with product engineering teams
- Build and improve scalable analysis pipelines and tooling for reliable insights at scale
- Create tools enabling Data Scientists and Engineers to independently access trusted datasets and metrics
- Identify data quality and discoverability gaps and advocate for improvements
- Maintain documentation for tools, datasets, metrics and operating practices
- Partner with Product, Engineering, Data Science and BI teams to communicate insights
What they're looking for
- SQL and Python/R programming
- Large-scale and high-dimensional dataset handling
- Data workflow orchestration and ETL/ELT pipeline development
- Window functions and query optimization
- Cross-functional collaboration and communication
- AI-assisted engineering with critical verification
- Data governance and quality assurance
- Analytics engineering or data solutions delivery
Opens the official application on the employer’s site. No login required.
Pinterest builds a large-scale platform serving millions of users, with infrastructure spanning security, database systems, and mobile products, supported by data systems and advertising technology. The company is hiring Software Engineers II and experienced engineers across security, infrastructure, iOS development, and data engineering to enhance platform capabilities, improve detection and response systems, optimize performance, and leverage AI-driven solutions.
- Website
- pinterest.com
Likely interview questions
- Walk us through a complex data pipeline you built—what challenges did you face with DAG dependencies or data partitioning and how did you solve them?
- Describe a time you translated ambiguous partner requirements into clear technical objectives. How did you validate you understood the problem correctly?