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
