Software Engineer, Data Platform

Software Engineer, Data Platform   Position Summary We are seeking a Software Engineer, Data Platform to help build and evolve the shared engineering foundations of our data platform. This role is responsible for developing platform capabilities, reusable engineering components, automation, and secure integration patterns that help teams build and operate trusted data products with greater consistency. The work spans platform engineering, data workflows, infrastructure automation, and secure systems integration. The ideal candidate brings strong software engineering fundamentals, experience building reliable systems in cloud environments, and familiarity with data platforms or data-intensive workflows. We value hands-on engineers who can own components end to end, from design and implementation through deployment, operational readiness, and continuous improvement. As part of our broader engineering direction, we are also moving toward a more AI-enabled developer experience. This includes modern development workflows and tools that improve productivity, while building the platform foundations needed to support practical AI-assisted engineering patterns over time.   Role Scope This is a hands-on software engineering role focused on building and improving shared platform capabilities. The role includes meaningful ownership of components and workflows, close partnership with other engineers, and strong expectations for implementation quality and operational readiness.   Key Responsibilities Build shared platform services, tooling, and automation that support trusted data products across teams and domains Develop reusable engineering patterns for ingestion, transformation, publishing, and operational workflows Build and maintain platform components, automation, and engineering utilities using Python and other appropriate technologies Contribute to internal tools and workflows that improve platform usability and reduce manual effort Partner with senior engineers to implement scalable, maintainable platform solutions Use infrastructure as code and CI/CD practices to improve consistency, repeatability, and maintainability Support secure integration patterns for internally managed tools and connected SaaS solutions Strengthen platform operability through observability, production-ready patterns, and practical automation Collaborate with engineering and cross-functional stakeholders to turn platform needs into reliable technical solutions Apply AI-assisted development practices where they improve engineering productivity and workflow efficiency Contribute to strong engineering standards through thoughtful implementation, code quality, testing, and peer review   Minimum Qualifications Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience 3+ years of software engineering, platform engineering, or related experience Strong programming experience in Python Experience building production-grade applications, services, APIs, internal tools, or platform components Experience working with cloud environments, including AWS or similar platforms Experience working with data platforms, data-intensive systems, or engineering workflows that support data products Experience designing and building systems with attention to reliability, maintainability, and operational supportability Familiarity with infrastructure automation, CI/CD practices, and modern software delivery workflows Familiarity with secure connectivity, networking concepts, and enterprise integration patterns Strong problem-solving skills and the ability to work effectively across engineering teams   Preferred Qualifications Experience with modern data platform technologies such as Snowflake, dbt, or similar tools Experience with Terraform, GitLab CI/CD, or similar infrastructure and delivery tooling Experience building internal platform tooling for engineering or data users Experience working on shared platform components or common engineering frameworks Experience integrating third-party SaaS tools into enterprise environments Experience with metadata, governance, lineage, or data quality workflows Experience improving developer experience through templates, automation, self-service, or platform tooling Experience with observability and operational practices for platform services and data workflows Experience using AI-assisted development tools such as Cursor, Claude, or similar tools in day-to-day engineering workflows Familiarity with MCP-style tooling or related engineering integration patterns   What Success Looks Like Reliable delivery of shared platform components and automation Improved usability and supportability of platform workflows Reusable engineering patterns that reduce duplication and manual effort Strong code quality, testing, and operational rea