Position : Senior Quantitative Developer - Systematic Strategies

Location: Navi Mumbai or Delhi

Shift: UK

The Role: As a Senior Quantitative Engineer, you will design, implement, and maintain the data and systems architecture that powers Morningstar's Systematic Strategies portfolios.For this you are expected to work closely with portfolio managers, researchers, and developers to help build, scale, and optimize the infrastructure that powers quantitative research and investment management.

You should understand the nuances of the data and prepare it for ingestion, and your daily work with researchers and portfolio managers will facilitate the research, design, and deployment of quantitative strategies.
This role is ideal for someone who thrives at the intersection of data engineering, cloud architecture, and financial systems. Data and Research are the key pillars for the role that require strong technical skills and comfort with cloud technology.

Responsibilities :

• Design and maintain scalable data pipelines for financial and alternative datasets using PySpark and AWS
• Architect and manage cloud infrastructure (AWS EMR, S3, Glue, Lambda, ECS) to support quant research and production
• Collaborate with quantitative researchers and portfolio managers to operationalize data workflows and support model deployment
• Develop and support interactive dashboards and internal tools to visualize pipeline health, system performance, and data quality metrics for quant teams
• Build and maintain tools and platforms for backtesting, simulation, and analytics.
• Optimize systems for performance, reliability, and cost-efficiency across compute and storage resources
• Implement data governance, quality, and lineage processes for high-integrity research environments
• Automate operational workflows and ensure CI/CD for quant systems

Requirements :

• Bachelor's or Masters' degree in a quantitative, financial discipline, or engineering.
• 5+ years of experience in a Quant Engineer / Data Engineer / Platform Engineer role in an investment data handling team
• Expertise in Python, with strong knowledge of PySpark and distributed data processing
• Hands-on experience with AWS services (EMR, S3, Glue, Lambda, ECS, CloudWatch)
• Familiarity with market data feeds and financial datasets like FactSet, Bloomberg, Compustat
• Strong understanding of data architecture, storage formats (Parquet, Delta), and Spark performance tuning
• Proficiency in developing interactive dashboards using tools like Tableau/PowerBI/Quicksight to monitor Portfolio performance
• Experience with orchestration tools like Step function/Airflow and containerization using Docker
• Comfortable working in Agile teams with Git, CI/CD, and infrastructure-as-code (Terraform, CloudFormation)
• Knowledge of SQL and distributed query engines (e.g., Athena)
• Exposure with Axioma or equivalent portfolio risk and optimization platforms (e.g., MSCI Barra, Bloomberg PORT, PyPortfolioOpt) to support risk modeling, portfolio construction, and performance attribution workflows
• Knowledge in the domains of Agile Methodology, Machine Learning, and Optimization is a plus

Good to have Skills:

• Strong understanding of Financial Reports like Returns/Factor Attribution/Risk metrices
• Exposure to financial markets or quant research environments.
• Familiarity with cost optimization strategies in cloud environments.
• Experience with real-time data ingestion frameworks (e.g., Kafka, Kinesis)

Morningstar is an equal opportunity employer.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person 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 in-office 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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