Senior Data Quality Assurance Engineer

*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.

*Collaboration: Strong ability to work with cross-functional teams, including data scientists, engineers, and financial experts, to enhance overall data quality.

Morningstar is an equal opportunity employer

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.