Design, build, and maintain secure, scalable backend services and REST APIs that enable metric consumption and power the user interface
Define and validate metric logic, perform exploratory and trend analysis, and translate findings into compelling dashboard narratives that inform engineering decisions
Partner with engineering teams, leaders, and stakeholders to align on performance benchmarks and translate shared goals into a coherent metrics strategy
Implement CI/CD pipelines, automated testing, secure coding standards, and code review practices to deliver production-grade, maintainable software
Deploy and operate services on cloud and container platforms, ensuring reliability, scalability, and operational excellence
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and advanced applied experience
Full-stack engineering capability with demonstrated experience building dashboards and web experiences end-to-end, spanning frontend, back-of-frontend, and backend integration
Experience working with SQL databases and analytical data stores at scale, including data warehouse concepts and query optimization
Demonstrated engineering practices including CI/CD pipeline implementation, automated testing, and code review standards
Analytical capability to develop and validate metrics — including exploratory analysis, cohort and trend analysis, and anomaly detection — and translate results into clear visual narratives
Effective communication and stakeholder partnership skills, with the ability to align technical and non-technical audiences around shared goals
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Preferred qualifications, capabilities, and skills
Experience with React and TypeScript building performant, accessible, data-rich dashboards and visualizations
Backend service and REST API development experience, including authentication and authorization, versioning, error handling, and performance optimization
Experience deploying and operating services on cloud or container platforms (e.g., AWS, Kubernetes, or equivalent enterprise platforms)
Experience integrating with Databricks to produce curated, dashboard-ready datasets, including layered data product patterns (e.g., bronze/silver/gold or equivalent) with clear contracts and quality controls
Hands-on experience with AWS services (e.g., ECS, S3, SQS, SNS, Kafka), PostgreSQL, and infrastructure-as-code tools such as Terraform
Prior experience building metrics or insight products for engineering productivity, developer experience, or technology transformation programs
Interest or experience in UX and data visualization design principles
