Purpose Own end-to-end features and production issues spanning the backend API layer and the frontend UI, with a specific charter to hunt down non-obvious performance bottlenecks (slow queries, N+1 patterns, pagination/caching mismatches, render-blocking UI work) and resolve complex, ambiguous defects that don't have a textbook fix. Key Responsibilities Design and build features across the layered backend and the feature-based frontend. Diagnose full-stack performance issues end-to-end — from AG-Grid/React Query data-fetching patterns in the UI, through FastAPI endpoint contracts, down to the database — and identify root cause rather than symptoms. Apply out-of-the-box thinking to intermittent, hard-to-reproduce, or cross-system defects: correlate logs, telemetry, and health-check data; form hypotheses; validate with targeted experiments instead of guesswork. Implement secure, tested endpoints following the response pattern (success/error), auth rules (JWT, role checks on mutating endpoints), and validation rules (Pydantic, 422 on invalid input). Build accessible, performant Carbon-based UI components with proper loading/error states, 4px-grid spacing, and scoped CSS. Write unit/integration tests (pytest for backend, Vitest/RTL for frontend) covering both correctness and performance regressions. Use GenAI code-generation and test assistants to accelerate delivery safely, with peer review and security checks on all AI-assisted output. Contribute to observability: structured logging, error handling, and lineage/traceability of data issues. Must-Have Skills Strong Python (FastAPI, Pydantic, raw SQL — no ORM) and strong TypeScript/React (Carbon Design System, Zustand, React Query, Vite). Proven track record diagnosing production performance issues: query optimization, connection pooling, pagination/caching design, and frontend rendering/data-fetching bottlenecks. Comfortable reading and correlating evidence across layers — API contracts, DB query plans, browser network/render traces, and application logs — to find root cause on ambiguous, cross-cutting bugs. Solid grasp of JWT-based auth, role-based access control, and secure coding practices (OWASP-aware). Experience with AWS services relevant to the stack (Lambda, Fargate, Secrets Manager). Testing discipline: pytest, Vitest/React Testing Library, and willingness to add regression tests for every non-trivial fix. Familiarity with automotive procurement / multi-tier supply chain domain concepts is a plus, not a blocker. KPIs / Success Measures Reduction in P95/P99 latency on flagged slow endpoints and UI views after intervention. Mean time to root-cause and resolve complex/ambiguous production defects. Escaped defect rate trending down; regression tests added per fixed bug. Story throughput and code quality (review turnaround, test coverage) maintained alongside performance work. Common Requirements (emphasis for this role) Emphasis: cross-layer performance diagnosis, root-cause analysis on complex/ambiguous issues, and disciplined, tested delivery across both backend and frontend. Proficient, responsible use of GenAI coding assistants (with unit tests, security checks, and peer review) to accelerate — not replace — sound engineering judgment. Quality, observability, and cost-awareness (FinOps) expected on all delivered work.