Software Engineer II, Data Analytics & Engineering

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

Job Title: Software Engineer II, Data Analytics and Engineering

Intro: 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.

What you’ll do:

• 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 ver