Staff Software Engineer, Infrastructure Data Engineering

Pinterest•New York, NY

3d•$177,185 - $364,795•Remote

About The Position

Pinterest is seeking a Staff Software Engineer to build data systems that help teams understand and manage infrastructure usage and cost. This individual-contributor role leads the design and hands-on delivery of reliable data products—including pipelines, models, and services—that support infrastructure planning, cost attribution, and budget workflows. The engineer will shape technical direction, navigate complex cross-team work, and improve the accuracy, consistency, and usefulness of infrastructure data. The role sets technical direction and helps define a roadmap for reliable, scalable infrastructure data products; designs, builds, and operates production ingestion and transformation pipelines, data models, and services; and strengthens shared data products through clear contracts, automated quality checks, reconciliation, documentation, and operational monitoring. It also leads ambiguous, cross-functional initiatives from problem definition and technical trade-offs through rollout and adoption, while raising engineering standards through mentorship and design reviews. The engineer is expected to use AI to improve individual and team impact, including investigating data-quality and pipeline issues, accelerating implementation and documentation of data transformations and interfaces, and exploring ways for partners to self-serve trusted infrastructure insights. AI-assisted work must be validated, sensitive information protected, and outputs verified. The role requires in-office collaboration 1–2 times per month and may be based anywhere in the country. It is not eligible for relocation assistance. The position is open to US-based applicants and is eligible for equity. The base salary range is $177,185–$364,795 USD.

Requirements

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.
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 day-to-day workflow for relevant outputs.
Strong track record of critically evaluating and verifying AI-assisted work, such as through testing, source-checking, data validation, and peer review.
High integrity and ownership, including protecting sensitive data, avoiding over-reliance on AI, and remaining accountable for final decisions and deliverables.

Nice To Haves

Experience with infrastructure cost or usage data, budgeting, shared data platforms, dbt, data lineage, or modeling hierarchical ownership.
Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale.

Responsibilities

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.
Use AI to accelerate investigation of data-quality or pipeline issues, validating findings against source data and production evidence.
Use AI to 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 while protecting sensitive information and verifying outputs.