What you will do
Develop intuitive planner experiences for configuring scenarios, managing assumptions, monitoring runs, comparing outcomes, and understanding uncertainty and trade-offs.
Build responsive, accessible React/TypeScript user interfaces and reusable frontend components for scenario setup, results visualization, and decision workflows.
Develop APIs, backend services, data integrations, and execution workflows for forecasting, simulation, optimization, and AI-assisted decision products.
Work closely with product managers, planners, data scientists, and adjacent engineering teams to translate business needs into clear, valuable product increments.
Participate actively in code reviews, technical design discussions, and continuous improvement of engineering practices.
Basic qualifications
Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.
5+ years of professional software-engineering experience building and supporting production applications.
Full-stack engineering experience, including modern frontend development with React and TypeScript and backend API development with Python or another modern server-side language.
Experience building usable, data-rich web applications in partnership with product, design, and business stakeholders.
Experience with relational SQL and data-intensive applications, including integration with warehouse, lakehouse, or operational data systems.
Familiarity with asynchronous processing, queues, workflow engines, or similar patterns for long-running workloads.
Experience with cloud deployment, containers, CI/CD, automated testing, and production troubleshooting.
Commitment to code quality, accessibility, documentation, observability, and operational ownership.
Preferred qualifications
Experience with FastAPI, Databricks, Spark, Kubernetes, modular frontend architectures, or comparable technologies.
Experience building interactive visualizations and comparison workflows for analytical, operational, financial, or planning users.
Experience embedding statistical models, optimization engines, simulation, or machine-learning capabilities into production applications.
Experience with API and data-contract versioning, enterprise identity and access management, and secure integrations.
Experience in supply chain, manufacturing, healthcare, finance, or another complex operational domain.
Familiarity with AI-assisted product experiences and appropriate grounding, evaluation, and human-review controls.
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