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: 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.