Backend Framework for OHLC Market Data Ingestion and Storage

Project 1: Backend Framework for OHLC Market Data Ingestion and Storage We need a backend developer to build a Python/PostgreSQL-based framework for collecting and storing historical OHLC market data for research purposes. The system should be able to: Download or import OHLC data in CSV format. Normalise the data into a consistent schema. Store securities, tickers, exchanges, sectors and industry classifications. Load large CSV datasets efficiently into PostgreSQL, preferably using a parallel PostgreSQL loader / COPY-based approach. Keep raw files and normalised files separately. Track import jobs, errors and basic data-quality issues. Provide initial FastAPI endpoints to query securities and historical OHLC data. Expected stack: Python PostgreSQL FastAPI SQLAlchemy / asyncpg or similar Docker / Docker Compose preferred Deliverables: Database schema. Data ingestion scripts. CSV normalisation process. PostgreSQL bulk-loading process. Basic FastAPI service. Short setup documentation. The goal is to create a clean backend foundation that can later be used for financial research and statistical analysis.