Senior Data Quality Assurance Engineer Job in Navi Mumbai at Morningstar, Inc.

Morningstar is hiring for the role of Senior Data Quality Assurance Engineer!

Requirements:

• Statistical Expertise: Advanced knowledge of statistical methods, including linear/non-linear modelling, hypothesis testing, and Bayesian techniques.
• AI/ML Integration: Strong skills in applying AI/ML algorithms (e.g., neural networks, random forest, anomaly detection) for data quality checks and predictive analysis. Experience with cloud-based environments (AWS, Azure, etc.).
• Quantitative Finance: Deep understanding of financial instruments, market data, and the use of quantitative methods in portfolio management and risk analysis.
• Programming Skills: Proficiency in statistical programming languages (Python, R, SQL) and experience with tools like MATLAB, SAS, or similar platforms.
• Automation: Experience in developing and implementing automated data validation processes, including real-time monitoring and alert systems.
• Data Governance: Strong knowledge of data management principles, regulatory compliance, and data governance practices, particularly in the context of financial services.
• Leadership & Mentorship: Ability to mentor and guide junior team members, sharing expertise in statistical analysis, AI/ML, and data quality best practices.
• Problem-Solving: Excellent analytical skills to identify root causes of data quality issues and implement long-term solutions.
• Collaboration: Strong ability to work with cross-functional teams, including data scientists, engineers, and financial experts, to enhance overall data quality.
• Statistical Expertise: Advanced knowledge of statistical methods, including linear/non-linear modelling, hypothesis testing, and Bayesian techniques.
• AI/ML Integration: Strong skills in applying AI/ML algorithms (e.g., neural networks, random forest, anomaly detection) for data quality checks and predictive analysis. Experience with cloud-based environments (AWS, Azure, etc.).
• Quantitative Finance: Deep understanding of financial instruments, market data, and the use of quantitative methods in portfolio management and risk analysis.
• Programming Skills: Proficiency in statistical programming languages (Python, R, SQL) and experience with tools like MATLAB, SAS, or similar platforms.
• Automation: Experience in developing and implementing automated data validation processes, including real-time monitoring and alert systems.
• Data Governance: Strong knowledge of data management principles, regulatory compliance, and data governance practices, particularly in the context of financial services.
• Leadership & Mentorship: Ability to mentor and guide junior team members, sharing expertise in statistical analysis, AI/ML, and data quality best practices.
• Problem-Solving: Excellent analytical skills to identify root causes of data quality issues and implement long-term solutions.