Sr. Engineer II - App Engg & AI

About the Role Senior engineer owning the API and data-serving layer of a customer-facing metrics and reporting platform — a React application, module-federated into a wider micro-frontend shell, backed by a FastAPI service over a curated analytics data layer. Because this is a reporting product, its value rests first on the metrics being correct and second on the experience that surfaces them; this role owns the first. This is a full-stack position with a backend centre of gravity. You will spend the majority of your time in Python, FastAPI, and PostgreSQL, and you are expected to be productive in the React and TypeScript codebase as well — picking up frontend stories, reviewing frontend pull requests, and shipping a feature end to end when delivery requires it. This cross-capability is a requirement of the role, not a preference. The team is deliberately structured so that no layer has a single owner, with every engineer able to take a feature through end-to-end development and support it in production. If you would prefer to stay within a single layer, this is not the right role. 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