Data Platform Engineer (Mid-level)

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Platform Engineer (Mid-level) based in United States. 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. Accountabilities - Improve CI/CD pipelines: Analyze existing deployment pipelines, determine the requirements of new tasks, and adapt established workflows to support reliable, repeatable software delivery. - Develop infrastructure-as-code: Extend and maintain infrastructure modules in line with existing repository patterns, ensuring consistency, reliability, and maintainability across services. - Apply data governance standards: Implement naming conventions, access controls, retention policies, and other governance requirements for new data services and infrastructure.

- Build self-service platform tooling: Develop and maintain a reusable template repository with preconfigured CI/CD, infrastructure-as-code, and governance standards so engineering teams can launch new pipelines with minimal manual intervention. - Create reusable modules: Build tools and modules that simplify pipeline creation and reduce repetitive engineering work, making common platform tasks accessible through standardized workflows. - Improve technical documentation: Write practical documentation, runbooks, and troubleshooting guides that help engineers understand and operate systems independently without relying on individual team members. - Own data access workflows: Design and manage access-request processes for data resources, reducing bottlenecks and enabling teams to obtain the permissions they need efficiently. - Take ownership of platform tooling: Progress toward full responsibility for the template repository and infrastructure modules, including technical direction, prioritization, maintenance, and roadmap decisions. - Collaborate across engineering disciplines: Work with data engineers, backend engineers, and infrastructure specialists to improve platform reliability and make shared tools useful across teams. - Use AI tools responsibly: Incorporate AI-assisted development tools into everyday workflows while validating generated code, identifying potential errors, and explaining the reasoning behind technical decisions. Requirements - 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.