Job Title

At Morningstar, we rely on powerfully insightful data to make decisions. Were seeking an experienced Senior Principal Data Engineer to put it to good use. The ideal candidate will have the expected organizing data by applying metadata concepts, combined with a problemsolving skill to achieve business goals. This person will wear many hats in the role, but much of the focus will be on building data structures to support effective services, eventdriven ETL/ELT processes and datadriven system design to support pub/subdriven solution design. In this role, you'll shape the longterm vision for how data is collected, stored, integrated, and used across the organization. Youll collaborate with data engineers, analysts, business leaders, and IT to ensure scalable, secure, and highperforming data systems that align with business goals. This position is based in our Toronto office. We follow a hybrid policy of at least 4 days onsite. Morningstar's hybrid work environment gives you the opportunity to collaborate inperson each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days inoffice 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. Objectives of this role Work with data structure to solve business problems, designing, building, and maintaining the infrastructure to answer questions and improve processes. Help streamline our data management and processing workflows, adding value to our service offerings, and building out data lifecycle models. Work closely primarily with Sales, Marketing and Finance teams to develop data models and pipelines to support technical service and business requirements. Be an advocate for best practices and continued learning. Responsibilities Work closely with our Sales, Marketing, Finance and Enterprise Data Platform teams to help build scalable data structure and datadriven ETL/ELT solution design that support pub/sub and eventdriven concepts. Evaluate and recommend modern data technologies, platforms, tools, and practices (Cloud data warehouses, data lakes, streaming platforms) to develop strategy for longterm data platform architecture. Provide guidance and mentorship to junior architects, engineers, and analysts to build a strong datadriven culture. Ensure data systems are optimized for scalability, performance, and costefficiency. Model frontend and backend data dependencies to help draw a comprehensive picture of data flows throughout the system and to enable effective data management and service definition. Design and implement metadatadriven data models that align with the organizations data governance framework and enable scalable, consistent, and efficient business intelligence solutions. Develop and maintain scalable data pipelines that support seamless service integrations and everincreasing data volume and complexity. Collaborate with crossfunctional teams in the organization to improve data models that feed business intelligence tools, increasing data accessibility, and fostering datadriven decision making across the organization. Implement processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders, services and business processes that depend on it. Perform data analysis required to troubleshoot data related issues and assist in the resolution of data issues. Integrate AI and machine learning capabilities into data architecture to enhance data quality, automate data management processes, and enable advanced analytics and predictive insights. Required Skills Bachelors or Masters degree (or equivalent) in computer science, information technology, engineering, or related discipline. 10+ years experience with Python, SQL, NoSQL, and data management tools. 10+ years experience in Objectoriented programming languages using mainstream programming languages (e.g., C#/.NET, Python, Java, etc.). 6+ years experience in Amazon AWS ecosystem and modern data warehouse tooling, including data loading tools (Airbyte, FiveTran, Informatica), data transformation tools (DBT), and metadata management tools (Atlan, Acryl DataHub). Excellent communication skills, especially for explaining technical concepts to nontechnical business leaders. Ability to work on a dynamic, and fastpaced team that has concurrent projects. Data pipelines and workflow management tools (e.g., AWS Glue, Airflow, Apache NiFi, etc.). Excellent problemsolving and organizational skills. Proven ability to work independently and with a team. Preferred Skills and Qualifications Experience in building or maintaining data structures, ETL and ELT processes at scale. Knowledge in AWS RDS, Redshift, ElastiCache, Glue, Kinesis, and Step Function are highly desired. Relevant professional certification is nice to have. Morningstar's hybrid work environment gives you the opportunity to collaborate inperson each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days inoffice 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. #J-18808-Ljbffr