Data Quality Engineer
Core
Design statistical methods to detect, characterize, and quantify data issues in financial datasets; ingest, clean, and maintain historical data from external sources to create pricing-grade inputs for quantitative researchers and traders.
Role type
Data Quality Engineer (Financial Data)
Builds
Automated data pipelines, statistical detection methods, and production-grade historical datasets
Domain
Financial markets, quantitative trading, data integrity
Deliverable
production ML models | product features
Required skills
Statistical methods design, time-series analysis, surface fitting, Python, data cleaning, anomaly investigation, pipeline automation
Preferred skills
Self-starter mindset, direct stakeholder communication, intellectual curiosity about market data
Technologies
Python, statistical libraries for time-series analysis
Responsibilities
Design statistical methods to detect outliers, distribution shifts, and inconsistencies; investigate anomalies and quantify their impact on pricing; refactor and generalize methods for new asset classes; design and own automated pipelines for historical data ingestion and cleaning; coordinate with researchers and traders on data trust and methodology; roll out methods to production across regions and asset classes
Seniority
Mid-level, hands-on IC