Morningstar

What we do:

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.

Job description:

The Data Quality Assurance Engineer will ensure data accuracy, integrity, and consistency within our data systems. This role involves developing quality frameworks, executing data quality checks, and performing advanced statistical validations. The candidate will implement AI/ML-based data checks to identify patterns and anomalies in financial and investment datasets. The role also includes automating data quality processes to maintain high standards of data governance and compliance with industry regulations.

Key Roles & Responsibilities:

• Develop and implement data quality frameworks and automated validation processes.
• Design and execute statistical checks on data to ensure accuracy and consistency across datasets.
• Perform routine checks on large datasets to detect anomalies, discrepancies, or inconsistencies.
• Perform root cause analysis on data discrepancies and drive resolutions.
• Develop and deploy AI/ML-based data quality checks for anomaly detection and trend analysis.
• Collaborate with data engineers and stakeholders to ensure data cleansing and transformation processes meet quality standards.
• Monitor and report data quality metrics and performance through automated systems.
• Participate in data governance and compliance initiatives to ensure adherence to industry standards and regulations.
• Support the continuous improvement of data quality processes by integrating machine learning and automation tools.
• Provide regular reports and dashboards on data quality metrics, identifying trends, risks, and areas for improvement.

Required Competencies:

• Technical Skills: Proficiency in SQL, Python, and data analysis tools. Experience with machine learning algorithms for data quality improvement. Cloud-based data platforms (AWS, Azure, Google Cloud) and modern data architectures.
• Statistical Analysis: Strong understanding of statistical checks, hypothesis testing, and anomaly detection.
• AI/ML Integration: Experience in developing AI/ML-based quality checks for large datasets.
• Automation: Knowledge of test automation frameworks and tools for continuous data validation.
• Attention to Detail: Ability to detect data issues and inconsistencies through thorough testing and analysis.
• Collaboration: Effective communication with cross-functional teams, including data engineers and business analysts.
• Financial Data Expertise: Familiarity with financial services, investment data structures, and related compliance requirements

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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