Full Stack Engineer, Data Science Focus with React

Role Summary We are looking for a Full Stack Engineer with a data science focus who also brings frontend experience with React. You will bridge the gap between data engineering and user-facing applications on Zula 2.0, the platform that ranks and fuses the output of our rail inspection systems - building both the backend data services and the interactive dashboards and tools that make inspection data accessible to stakeholders across the rail business. This is the seat that decides what our analysts and customers actually see. Zula 2.0 is the critical path for the team through the end of the year. What We Expect From You We expect an exceptional level of drive and ambition. You think beyond today’s work to what the team and organization need next, champion bold ideas, and see them through. Your hunger is infectious - it inspires those around you to aim higher. You should be someone who puts the team first. You share credit openly, admit when you are wrong, and welcome feedback as an opportunity to grow. You are comfortable saying “I don’t know” and asking for help when needed. Strong interpersonal skills are important for this role. You should have good instincts for working with different people, listen actively, navigate disagreements constructively, and communicate clearly with both technical and non-technical audiences. You should be able to quickly grasp complex problems that span multiple systems or domains. We expect you to design effective solutions for non-trivial requirements, identify root causes efficiently, and consider performance, scalability, and maintainability in your approach. Key Responsibilities Design and develop data pipelines, ETL processes, and backend services using Python Build interactive dashboards, data visualization tools, and web applications using React Develop RESTful APIs that connect data services to frontend applications Work with large-scale rail inspection datasets including ultrasonic, electromagnetic, and operational data Implement and deploy machine learning models and statistical analyses in production environments Architect and manage AWS cloud infrastructure for data workloads (S3, Lambda, Glue, Athena, SageMaker, Step Functions) Collaborate with domain experts to translate rail inspection requirements into technical solutions Write clean, tested, well-documented code following engineering best practices Participate in code reviews, sprint planning, and technical design discussions Collaborate with cross-functional teams including inspection engineers, data scientists, and product managers Contribute to a culture of continuous improvement, knowledge sharing, and technical excellence Required Skills & Qualifications Strong proficiency in Python (NumPy, Pandas, Scikit-learn, or similar) Experience with React (hooks, state management, component architecture) Experience building data pipelines and working with structured and unstructured data Solid understanding of SQL and relational and non-relational databases Experience with AWS cloud services (S3, Lambda, EC2, RDS, Glue, or similar) Familiarity with modern frontend tooling (TypeScript, Webpack/Vite, CSS frameworks) Understanding of software engineering principles (testing, code quality, design patterns) Familiarity with version control (Git), CI/CD pipelines, and agile development practices Strong problem-solving skills and ability to learn new technologies quickly Good communication skills - able to explain technical concepts to non-technical stakeholders A collaborative, team-first mindset aligned with our values of being Humble, Hungry, and Smart Desirable Skills Experience with data visualization libraries (D3.js, Recharts, Plotly) Exposure to time-series data, geospatial data, or IoT data streams Experience presenting complex or uncertain data to non-specialist users Experience in rail testing, NDT, or sensor-based inspection industries (ultrasound, eddy current, electromagnetic, etc.) AWS certifications Experience with infrastructure-as-code (CloudFormation, CDK, Terraform) Knowledge of containerization (Docker, ECS/Fargate) #J-18808-Ljbffr