Full Stack Engineer

Key Responsibilities

• Own the metrics API — a FastAPI service exposing tenant-scoped metrics over curated KPI data, including aggregation logic, query performance, and schema evolution
• Enforce multi-tenant isolation — tenant and billing-entity scoping implemented structurally in the data access layer, not left to individual queries
• Design and evolve the data model — SQLModel entities and Alembic migrations run safely against live data, with validation queries accompanying every schema change
• Own the API contract — Pydantic models on the server matched by Zod schemas on the client, with no silent drift between the two
• Guarantee metric parity — reconcile what the platform reports against the existing trusted BI numbers, and be able to prove and explain any figure that is questioned
• Tune performance — caching strategy and invalidation, rate limiting, pagination over large result sets, and query plan analysis
• Implement authentication and authorization — OAuth2 / OIDC integration, JWT validation, scopes and claims
• Build and review frontend work — deliver React and TypeScript features to the team standard, review frontend pull requests, and take end-to-end ownership of a slice when the work calls for it
• Own quality — pytest coverage on all logic that computes or scopes a number, plus tracing and structured logging sufficient to diagnose issues in production
• Support the application in production — triage and resolve customer-raised issues across the stack, working from logs, traces, and metrics rather than guesswork
• Partner with data and analytics — work with the data engineering and BI functions on metric definitions, mart design, and reconciliation

Qualifications

• 6+ years building production backend services; 3+ years with Python in a modern async stack
• Working proficiency in TypeScript and React (required) — able to build and review frontend features to the team standard.
• Deep FastAPI experience — async request handling, dependency injection, background tasks
• Strong PostgreSQL: async access via asyncpg, connection pooling, indexing, and the ability to read and fix a query plan
• Advanced SQL for analytics — window functions, CTEs, time-bucketed rollups, and correct handling of late-arriving and duplicate data
• SQLModel or SQLAlchemy, and Pydantic for modelling and validation
• Alembic migrations, including reversible migrations applied to live production data
• Multi-tenant application experience with row- or entity-level scoping and isolation guarantees
• OAuth2 / OIDC, JWT, and API authorization patterns
• Caching, rate limiting, and API performance tuning under real load
• pytest, async test patterns, fixtures, and testing against a real database rather than mocks
• Familiarity with reporting or analytics products, and comfort reasoning about whether a number is correct
• Git-based workflow, code review discipline, and CI/CD
• Cloud platform experience (Azure preferred), containerization, and production observability such as OpenTelemetry, Prometheus, or Loki is a plus
• Exposure to dbt, Snowflake, or warehouse-to-application data modelling is a plus
• Power BI or DAX literacy — enough to read a metric definition and reconcile against it — is a plus
• Strong written communication and the ability to work within defined overlap hours across time zones