Real systems built to production standards — pipelines, warehouses, ML workflows, and data science work across finance, sport, and urban data.
Real-time London transport data on your laptop. Airflow ingests TfL bus and tube arrivals and stores them in Parquet. dbt and DuckDB handle transformation, Great Expectations validates every batch, and OpenLineage provides full data lineage tracking — a completely observable, production-pattern lakehouse with zero cloud dependency.
Production-style Change Data Capture pipeline: Postgres → Debezium → Redpanda (Kafka-compatible API) → ClickHouse. Includes a synthetic e-commerce event generator, Python CDC sink, and dbt models with tests for analytical queries on captured changes.
Local-first lakehouse ingesting OpenAQ air quality and Open-Meteo weather data. Airflow orchestrates ingestion, raw JSON lands in MinIO (S3-compatible), and dbt applies incremental models with full validation in PostgreSQL.
Formula 1 analytics warehouse built on an Ergast-compatible schema, converting raw race timing data into KPIs for pit stop performance, strategy effectiveness, and reliability. Dimensional modelling with dbt and full CI via GitHub Actions.
End-to-end football analytics warehouse on BigQuery, built with dbt and GitHub Actions CI. Covers Premier League seasons 2014–15 to 2024–25 from openfootball JSON, modelled into analytics-ready dimensional tables.
Local-first, portable lakehouse demo using dbt and DuckDB. Features versioned seeds, staging-to-marts models, schema tests, auto-generated documentation, and a GitHub Actions CI example — ideal for learning and prototyping analytics engineering patterns.
End-to-end ML pipeline for financial time-series forecasting using LSTM deep learning. Achieves an R² of 0.95, mean absolute error of $2.4, and 87% directional accuracy. Covers data ingestion, feature engineering (technical indicators, rolling stats, lag features), LSTM architecture design, hyperparameter tuning, and rigorous backtesting methodology.
Interactive notebook clarifying MCAR, MAR, and MNAR missingness mechanics; NumPy masked arrays; and modern imputation using scikit-learn KNN and Iterative/MICE imputation. Includes reproducible experiments and visual analysis.
Notebook exploring core probability theory: binomial distributions, the Law of Large Numbers, the Central Limit Theorem, and Chebyshev's inequality with Uniform/Normal/Gamma tail comparisons — all implemented and visualised from scratch.
Implements OLS linear regression from the ground up — slope/intercept derivation, loss function, gradient descent, and model evaluation with visualisation. Demonstrates core ML foundations without library abstractions.
Systems design, UI prototyping, and foundational programming work.