Data Engineer for Football Historical Tick Data

We are seeking a skilled Data Engineer to develop a data pipeline for historical football tick data. The role involves designing and implementing a system to collect, process, and store large volumes of data. The ideal candidate will have experience in data engineering and a strong understanding of data structures and algorithms. Familiarity with cloud platforms and data visualization tools is a plus. I am building a rigorous quantitative backtesting operation and require a highly skilled data engineer to process and structure historical in-play tick data for European major leagues. The final deliverable must be a clean, timestamped dataset ready for ingestion into a PostgreSQL/TimescaleDB architecture. The Scope of Work: I require a complete dataset of high-frequency, tick-by-tick exchange odds for a full recent season (including the final stretch up to April 2026). The data must be parsed into a clean relational format. The dataset must include: Pre-match and in-play odds for the Match Odds (1X2) market. Precise timestamping (to the millisecond) to accurately evaluate time decay. The Bid-Ask spread (best back/lay prices) and the matched volume/liquidity available at each tick. Live score state cleanly mapped to the exact market timestamps. Deliverables: Data Processing: You will process raw historical exchange data (e.g., JSON stream files) and parse it into a clean, normalized structure. (If you have access to a legally compliant B2B historical data feed, please specify). Database Integration: Provide the necessary Python scripts to efficiently format and bulk-load this massive dataset into a local PostgreSQL database optimized with TimescaleDB. Data Validation Script: Write a sample Python backtest script utilizing pandas and psycopg2.