Top Data & Analytics Jobs

Lead architecture and implementation of a unified, cloud-native data feed platform. Design scalable ingestion, transformation, and delivery pipelines; establish DataOps, governance, monitoring, CI/CD, and performance best practices; mentor engineers and influence cross-team technical strategy.

Build scalable data pipelines and investment data platforms supporting quantitative research, backtesting, and deployment of multi-asset investment models. Responsibilities include processing market and fundamental data, integrating third-party providers, maintaining databases and research datasets, automating workflows with CI/CD and infrastructure-as-code, and supporting cloud-based and event-driven architectures. The role collaborates closely with quantitative researchers and portfolio managers and explores AI/ML and generative AI applications.

Collects, maintains, and validates portfolio data across global markets, ensuring accuracy, completeness, coverage, and timely delivery. Collaborates with global data teams, fund companies, clients, and third-party providers. Improves procedures through analytical tools, automation, Lean Six Sigma, and process-improvement projects. Provides expertise on investment data calculations, supports portfolio template testing, handles data transfer agreements, and monitors relevant financial regulations.

Leads Morningstar’s Investment and Time Series team, overseeing quantitative methodology, financial analytics, time-sensitive investigations, and larger research initiatives. Serves as the technical methodology authority, reviews logic, ensures quality, coordinates with Product, Data, Technology, and market stakeholders, improves documentation and AI-assisted validation, and mentors quantitative researchers. Requires broad expertise in investment analytics, time series, asset allocation, modern data tools, and team leadership.

Analyzes product, visitor, subscriber, acquisition, engagement, and retention data across Morningstar’s investor products. Builds KPI dashboards, automated reporting, forecasting and financial models, cohort and lifetime-value analyses, and SQL-based data pipelines. Partners with engineering and data teams to improve tracking, data infrastructure, and reporting accuracy. Uses insights to optimize pricing, marketing spend, customer acquisition, product engagement, and retention strategies.

Develop and maintain quantitative credit risk models and analytical tools for structured finance transactions, including ABS, CMBS, RMBS, and structured credit. Responsibilities include proprietary research, Python and C++ library development, statistical and predictive modeling, large-scale data analysis, model validation, analytics solution design, and technical research writing. The role collaborates with credit ratings, methodology, data engineering, and technology teams to improve rating methodologies and support analyst decision-making.

Research and analyze managed investment data, translating business methodologies into data, process, and technology solutions. Design data flows, interfaces, backend logic, and quality controls; conduct feasibility and impact analyses; document requirements; coordinate implementations; communicate risks; and support data-processing improvements across global teams.

Processes and maintains global investment data, ensuring accuracy, completeness, coverage, and timely delivery. Collaborates with global data teams, fund companies, and internal clients; manages data acquisition relationships and issue resolution. Develops analytical tools, improves procedures, documents use cases, and translates client requests into specifications and user stories. The role requires fluent English and French, financial industry knowledge, strong organizational skills, and proficiency in Microsoft Excel.

Support global data team collecting, organizing, and verifying financial data across markets. Assist with acquisition channels, resolve data issues, document client requests, and apply project management practices (LEAN Six Sigma) to improve workflows and meet production deadlines.

Performs Structured Finance security mappings, data quality controls, reconciliations, regulatory reporting checks, and dataset validation. Maintains metadata, data dictionaries, and data lineage for governance and AI-ready initiatives. Investigates and documents data issues while collaborating with Data, Credit Operations, Technology, and global stakeholders. Supports accurate, complete, timely, and compliant ratings and regulatory datasets.