The role involves improving and maintaining tooling and infrastructure for Python development, focusing on workflow efficiency and reliability in a collaborative team environment.
We build and maintain the foundational tools and infrastructure that power Plaid’s engineering velocity. Our mission is to eliminate developer friction, ensure system reliability, and empower product and ML engineers to ship faster with confidence.
As a Python Infrastructure engineer on the Developer Efficiency team, you will drive critical improvements to the tools and workflows that power Plaid’s Python development ecosystem. You will lead initiatives to simplify, standardize, and scale how Python code is authored, built, tested, and released.
Responsibilities
- Improve Plaid’s Python monorepo by contributing to workflows and best practices that boost developer velocity and code quality.
- Build and maintain developer tools that standardize how Python code is authored, tested, and released.
- Improve and operate a fast and reliable CI pipeline that scales with the monorepo.
- Create zero-setup, on-demand development environments to accelerate onboarding and streamline engineers’ day-to-day workflows.
- Work with product, infrastructure, and ML engineers to identify friction points and deliver pragmatic, reusable improvements to the developer experience.
- Collaborate with the broader Platform team to deliver secure, maintainable, and intuitive infrastructure that supports Plaid’s growth.
Qualifications
- 3+ years in platform or infrastructure engineering focused on Python
- Hands-on experience maintaining Python at scale, especially in multi-tenant monorepos.
- Strong understanding of Python packaging, dependency management, and best practices for shared library development.
- Experience designing developer tooling and standardised developer workflows.
- Strong cross-functional communication skills.
- Familiarity with Go and infrastructure tooling (Docker, Terraform, AWS).
- Contributions to the Python open-source ecosystem (PyPA, packaging, PEPs, etc.).
- Familiarity with ML/AI development workflows and the tooling that supports them.
- Experience with monorepo management tools such as Bazel or Pants.
Nice to Haves:
Top Skills
AWS
Docker
Python
Terraform
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