Senior Data Quality Assurance Engineer

Morningstar is a financial services company committed to helping people achieve financial security through trusted investment research and data. Our Managed Investment Data (MID) team plays a crucial role in this mission by working directly with asset management companies, which send us comprehensive data on their funds. This data includes information on portfolios, ownership stakes, investment styles, NAVs (net asset values), holdings, and operations. Our team's responsibility is to collect, organize, and standardize this data, adding value with Morningstar's own analytics to help investors make better-informed decisions.
The work of the MID team supports individual investors, financial advisors, and institutional clients by ensuring they have access to clear, accurate, and compliant investment data across Morningstar's software and data platforms. Since 2020, the team has grown significantly, expanding from just five people to over 380. This growth reflects the increasing importance of our work and the high demand for reliable managed investment data in the financial industry. By managing new fund activations and essential documentation, the MID team helps ensure data accuracy and regulatory compliance, which are essential for effective fund management and supporting the broader financial ecosystem.
Responsibilities
The Senior Data Quality Assurance Engineer will be responsible for ensuring the accuracy, integrity, and consistency of data across our systems. This role involves designing and implementing quality frameworks, conducting data quality assessments, and performing advanced statistical validations. The ideal candidate will leverage AI/ML techniques to detect patterns and anomalies in financial and investment data. Additionally, this position focuses on automating data quality processes to uphold robust data governance and ensure compliance with industry regulations.

• Lead the design and implementation of quantitative data quality frameworks, including statistical checks and anomaly detection systems.
• Utilize advanced statistical methods (e.g., linear and non-linear modelling, Bayesian analysis) to evaluate data quality across large, complex datasets.
• Develop and integrate AI/ML models for predictive data quality checks and to improve data accuracy over time. Improvement in data collection
• Ensure compliance with financial regulations and industry standards related to data governance, including managing data privacy and security risks.
• Mentor junior quantitative analysts, promoting best practices in data quality management and statistical analysis.
• Communicate findings, data quality trends, and proposed solutions to senior leadership, ensuring data-driven decision-making.
• Lead the creation and maintenance of automated test scripts to improve test efficiency.
• Ensure continuous integration of automated tests into the CI/CD pipeline.
• Identify gaps in testing coverage and propose solutions.

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.

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.
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