Data Quality Assurance Engineer

The Group: Equity Operations - Global Quality & Process Engineering
The Global Quality & Process Engineering works with teams across the company to help transform and improve our quality, practices, processes, and methods. Each member of this team serves to enhance customer experience by using philosophies and frameworks like Lean, Agile, Design Thinking, Diagnostic Approach, Client Centricity by using state-of-art technologies like Machine Learning, Deep Learning, Programmatic Anomaly Detections. This team stays abreast of emerging technologies, people practices, and other concepts related to the "future of work" and help introduce them in the organization.
The Team: The pursuit of quality at Morningstar is deeply ingrained in our culture and it is a key part of the value we bring our clients. Measures, builds, sustains, and owns the processes across the company that help us deliver quality data, products, and services. Quality control workstream, a sub-team of Global Quality & Process Engineering provides independent, comprehensive and client focused measurement on database through audit projects and insights on quality improvement that help us deliver quality data, products, and services.
The Role: The team seeks a self-motivated individual to help develop and implement data quality projects to maintain and improve the quality of Morningstar databases. This role will be closely working with Technology & Quants, Clients, Methodologies reflecting Accuracy, Consistency in quality & Process improvement projects.
Responsibilities:

• Ideate & build next generation of data science driven proactive quality checks.
• Understand the business process, analyze the multiple data sources/systems, translate business requirements into technical specifications and subsequently work with business stakeholders.
• Acting as a functional subject matter expert for partnering to troubleshooting data issues, redesign processes and develop reports.
• Understand and frame quality checks around data quality & extensively working with the data engineers & statisticians to create & maintain the data quality.
• Develop new tools and capabilities to perform data quality analytics independently, including the use of statistical and machine learning techniques.
• Work closely with multiple global & local teams across various functions and participate regularly in global calls and meetings.
• Retrospect and look for continuous process improvement.

Requirements

• Professional or master's degree in mathematics/Statistics/Finance with 4+ years of experience.
• AWS expertise in end to end (application data base design + ETL /pipeline + Visualization Reporting) leveraging AWS technologies such as Cloud, Athena, Glue, Lambda, EC2, S3, etc.
• Good to have skills/experience in SQL, Python, Apache Spark etc.
• Understanding of Data modelling and Schema Design principles
• Building Machine learning Models including Supervised, Unsupervised.
• Performing exploratory data analysis to support hypothesis & applying different engineering features and selection techniques.
• Work with developers. Data analysts, business analyst and architects to guide the implementation and validation of solution at scale in big data production environment.
• Experience, interest, and adaptability to working in an Agile delivery environment.
• Ability to work in a fast-paced environment where change is a constant and ability to handle ambiguous requirements.
• Some progress towards CFA, CPAs or CAs will be preferred not mandatory.
• Understanding of capital markets and its functioning is an advantage.
• Strong project management skills in initiating large data quality projects and presenting results to stakeholders.
• Strong intra or cross functional teams' communication.
• Works cohesively with team members in pursuit of team & project goals. Participates in group activities within the team. Builds stable working relationships internally.

Morningstar is an equal opportunity employer.
100_MstarResCanad Morningstar Research, Inc. (Canada) Legal Entity
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.