Staff Software Engineer, Infrastructure Data Engineering

Before you apply

Job source

Ats

Applying on

pinterestcareers.com

Workplace

Fully remote

US state

NY

Category

IT

Link last checked

4 hours ago

Posted

1 week ago

Closes

No stated closing date

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About the role

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 . Pinterest is seeking a Staff Software Engineer to build the data systems that help teams understand and manage infrastructure usage and cost. You’ll lead the design and hands-on delivery of reliable data products, including pipelines, models, and services, that support infrastructure planning, cost attribution, and budget workflows. In this individual-contributor role, you’ll shape technical direction, navigate complex cross-team work, and make infrastructure data more accurate, consistent, and useful. What you’ll do: Set technical direction and help define a roadmap for reliable, scalable infrastructure data products Design, build, and operate production ingestion and transformation pipelines, data models, and services for infrastructure usage, cost, ownership, and planning Strengthen shared data products with clear contracts, automated quality checks, reconciliation, documentation, and operational monitoring Lead ambiguous, cross-functional initiatives from problem definition and technical trade-offs through rollout and adoption; raise the engineering bar through mentorship and design reviews Leverage AI to scale the impact of yourself and the team, including: Accelerate investigation of data-quality or pipeline issues while validating findings against source data and production evidence Speed up implementation and documentation of data transformations and interfaces while testing and reviewing the results Explore ways to help partners self-serve trusted infrastructure insights, protecting sensitive information and verifying outputs What we’re looking for: Bachelor’s degree in computer science, a related field, or equivalent practical experience Significant experience designing, building, and operating production data systems or data-intensive services at scale Strong software engineering skills in Python and SQL, with a record of writing tested, maintainable production code Deep experience with ingestion, large-scale ETL and transformation, data modeling, and workflow orchestration Experience designing data interfaces and contracts and improving quality, freshness, completeness, and consistency A track record of leading complex technical work across teams, from framing ambiguous problems to delivery and operation Ability to influence through technical judgment and clear communication and to mentor other engineers Experience operating dependable data pipelines or services, including testing, monitoring, and incident response Experience with AWS or similar cloud environments Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review) High integrity and ownership: you protect sensitive data, avoid over-reliance on AI,

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