Full Stack Engineer

Key ResponsibilitiesOwn the metrics API — a FastAPI service exposing tenant-scoped metrics over curated KPI data, including aggregation logic, query performance, and schema evolutionEnforce multi-tenant isolation — tenant and billing-entity scoping implemented structurally in the data access layer, not left to individual queriesDesign and evolve the data model — SQLModel entities and Alembic migrations run safely against live data, with validation queries accompanying every schema changeOwn the API contract — Pydantic models on the server matched by Zod schemas on the client, with no silent drift between the twoGuarantee metric parity — reconcile what the platform reports against the existing trusted BI numbers, and be able to prove and explain any figure that is questionedTune performance — caching strategy and invalidation, rate limiting, pagination over large result sets, and query plan analysisImplement authentication and authorization — OAuth2 / OIDC integration, JWT validation, scopes and claimsBuild 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 itOwn quality — pytest coverage on all logic that computes or scopes a number, plus tracing and structured logging sufficient to diagnose issues in productionSupport the application in production — triage and resolve customer-raised issues across the stack, working from logs, traces, and metrics rather than guessworkPartner with data and analytics — work with the data engineering and BI functions on metric definitions, mart design, and reconciliationQualifications6+ years building production backend services; 3+ years with Python in a modern async stackWorking 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 tasksStrong PostgreSQL: async access via asyncpg, connection pooling, indexing, and the ability to read and fix a query planAdvanced SQL for analytics — window functions, CTEs, time-bucketed rollups, and correct handling of late-arriving and duplicate dataSQLModel or SQLAlchemy, and Pydantic for modelling and validationAlembic migrations, including reversible migrations applied to live production dataMulti-tenant application experience with row- or entity-level scoping and isolation guaranteesOAuth2 / OIDC, JWT, and API authorization patternsCaching, rate limiting, and API performance tuning under real loadpytest, async test patterns, fixtures, and testing against a real database rather than mocksFamiliarity with reporting or analytics products, and comfort reasoning about whether a number is correctGit-based workflow, code review discipline, and CI/CDCloud platform experience (Azure preferred), containerization, and production observability such as OpenTelemetry, Prometheus, or Loki is a plusExposure to dbt, Snowflake, or warehouse-to-application data modelling is a plusPower BI or DAX literacy — enough to read a metric definition and reconcile against it — is a plusStrong written communication and the ability to work within defined overlap hours across time zones