With 25 years as the premier provider of historical options and implied volatility data, OptionMetrics distributes its IvyDB options, futures, and dividend forecast databases to leading portfolio managers, traders, and quantitative researchers at 350 corporate and academic institutions worldwide to construct and test investment strategies, perform empirical research, and assess risk.

About the Role:

OptionMetrics is seeking a detail-oriented Data Quality Analyst to support the integrity, accuracy, and reliability of our financial data products. This role will work closely with Quants, Data Engineering, and Development teams to validate complex datasets, identify anomalies, and ensure our data meets the highest standards expected by institutional clients.

Key Responsibilities:

Perform data validation checks across large financial datasets, including options, futures, and equity data.

Analyze and investigate discrepancies, unexpected movements, and data anomalies.

Develop and maintain data quality rules, validation frameworks, and monitoring processes.

Partner with Quantitative Research teams to translate model logic into actionable validation specifications.

Execute historical data analysis to identify trends, inconsistencies, and edge cases.

Document findings, validation methodologies, and data quality standards clearly and concisely.

Contribute to automation of QA processes using SQL, Python, or similar tools.

Qualifications:

Bachelor’s degree in Finance, Mathematics, Computer Science, or a related field.

2–5 years of experience in data analysis, preferably in financial services.

Strong Python skills are required, with the ability to write clean, efficient, and production-quality code (this will be assessed through a live coding interview.)

Strong SQL proficiency is required, including working with large datasets (also assessed via live coding.)

Strong analytical thinking and problem-solving skills.

High attention to detail and ability to work independently.

Excellent technical writing skills are required, with the ability to clearly document data logic, validation methodologies, and findings for both technical and non-technical audiences.

Understanding of financial markets is a plus.

Nice to Have:

Experience with market data vendors or financial datasets (e.g., options, volatility surfaces.)

Familiarity with statistical techniques (e.g., z-score, outlier detection.)

Exposure to data pipelines and ETL processes.