Teams at Morningstar

Designs and maintains automated data quality frameworks, validation controls, anomaly monitoring, and source-to-target reconciliation processes for large-scale financial datasets. The role validates data from market-data providers, embeds quality controls into ETL/ELT pipelines, supports new dataset onboarding, and performs profiling and root-cause analysis. Requires advanced Python, SQL, and PySpark skills, financial data expertise, and exposure to AWS-based platforms and AI productivity tools.

Develops investment analytics, portfolio monitoring, performance measurement, risk reporting, benchmark analytics, and financial data dashboards. Builds scalable reporting solutions using Power BI, Tableau, or Qlik Sense, and processes large multi-asset datasets with Python, PySpark, and SQL. Automates reporting, integrates APIs and financial data providers, and collaborates with investment and technology teams to support portfolio management and investment decisions.

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.