Data Platform Engineer (Mid-level)

In this role, you will help transform an established data and machine learning platform into a scalable, self-service environment that empowers engineering teams to work independently. You will build and improve deployment pipelines, infrastructure-as-code modules, and reusable tooling that makes data services easier to launch, manage, and maintain. Working across data engineering, backend development, and infrastructure, you will contribute to reliable systems that support a global shipping and fulfillment platform. You will join an experienced Data & AI team with established engineering standards, giving you a solid foundation to make a tangible impact from your first weeks. The position offers increasing ownership, with the opportunity to lead the roadmap for shared platform tooling within your first six months. You will also work in an AI-enabled engineering culture that values technical judgment, accountability, and the ability to explain and validate the code you deliver.

• Deployment ownership: Demonstrated experience independently owning a deployment path end to end, including building a pipeline, troubleshooting failures, resolving production issues, and documenting the solution in a runbook.

• Professional experience: Approximately two years or more of professional experience in software, data, platform, DevOps, or infrastructure engineering.

• Technical education: A degree in Computer Science, Engineering, or a related field.

• Python and SQL: Practical working knowledge of both languages and the ability to use them regularly in professional engineering tasks.

• CI/CD and infrastructure-as-code: Experience working with deployment pipelines and infrastructure-as-code practices, with the ability to understand, adapt, and improve existing implementations.

• Code analysis and troubleshooting: Ability to navigate an unfamiliar codebase, explain how it works, identify potential failure points, and make sound technical judgments.

• Containers and cloud: Experience with Docker and familiarity with cloud computing concepts, particularly AWS. Previous experience with specific services such as SageMaker is not required.

• Cross-functional technical skills: Willingness and ability to work across data engineering, backend systems, and infrastructure rather than focusing exclusively on one area.

• AI-assisted development: Comfortable using AI coding assistants such as Copilot, Claude, or Cursor. You must be able to review generated code critically, explain why it is correct, and recognize when it is inaccurate or unsafe.

• Ownership and accountability: A proactive mindset, willingness to take responsibility for systems you modify, and honesty about failures, root causes, and lessons learned.

• Respect for established patterns: Ability to understand existing architecture and engineering conventions before deciding what should change.

• Communication and reasoning: Comfortable explaining technical decisions, defending implementation choices, and being evaluated on reasoning as well as delivery.

• Learning mindset: Motivated to develop new skills and explore unfamiliar tools. Prior experience with every technology or the specific business domain is not required.