> ## Documentation Index > Fetch the complete documentation index at: https://offthepace.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # About: skills demonstrated and the builder behind Off The Pace > Off The Pace mapped to concrete data-engineering competencies, each linked to its evidence in the repo: data modelling, data contracts, orchestration, ML engineering, CI/CD, IaC, observability, and cost. Off The Pace is a portfolio project built to clear a production data-engineering bar end to end, from raw API pulls to a deployed, monitored, browser-native app. This page maps the work to the competencies a reviewer is grading for, with a link to the real artifact behind each one. Nothing here is a keyword without an artifact attached. ## Skills demonstrated | Competency | Demonstrated by | Evidence | | ---------------------------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------- | | **Data modelling** | Medallion warehouse (bronze → silver → gold), dimensional marts, 60 dbt models in one topological pass | [Transform layer](/transform/overview) | | **Data quality & contracts** | 443 dbt tests, the CI-enforced additive-identity invariant, schema contracts | [The CI Contract](/transform/ci/overview) | | **Orchestration** | Staged pipeline DAG with retries, source-freshness checks, and build-over-build profiling | [Data pipeline](/platform/data-pipeline) | | **ML engineering** | XGBoost trained and exported to ONNX with train/serve parity and calibrated intervals | [ML layer](/ml/overview) | | **CI/CD** | Deploy / promote / rollback, PR preview channels, drift gates as merge blockers | [Deployment](/platform/deployment) | | **Infrastructure as code** | Terraform for the GCS bucket, Workload Identity Federation, and a billing budget | [Architecture decisions](/platform/architecture-decisions) | | **Observability & cost** | Sentry, web-vitals RUM, synthetic monitors, SLOs, and a zero-server cost model | [Observability](/platform/observability) · [Performance](/platform/performance) | For the narrative version of how these fit together, see [Engineering highlights](/engineering-highlights) (the sixty-second tour) and [How this clears a production bar](/data-engineering) (the deep read). ## What this project is A full-stack F1 analytics platform that answers a question lap time alone cannot: when a car is off the pace, exactly why? It ingests seven seasons of telemetry, models it through a dbt + DuckDB warehouse, trains five ML models, and serves 30 interactive features in the browser with no server, no login, and no per-user serving cost. Every lap is decomposed into seven additive, physically-grounded components, so lost time is attributed to a named cause rather than a feeling. The engineering thesis is that the interesting numbers should be *enforced*, not asserted: the seven-term decomposition closes to zero on every lap because a CI test fails the build otherwise, and the model that serves in the browser is provably the model that trained because ONNX parity is tested to `atol=1e-5`. ## About the builder Built by Justin Clarke. The project doubles as a working demonstration of an end-to-end data and ML platform: data contracts and lineage, reproducible drift gates, ML with calibrated uncertainty and honest out-of-sample validation, CI/CD with rollback, infrastructure as code, and observability with SLOs. More work and contact details. The full source: ingestion, transform, ML, app, and infra. All 30 features, running client-side.