Quantitative Developer

Role: Quantitative Developer

Location: Mumbai/ Delhi (4 days working from office)

Shift: UK Shift (Afternoon Shift) (2:00pm/2:30pm to 11:00pm/11:30pm)

The Area: The Research & Investment group is a global team guided by Morningstar’s investment principles focused on delivering great long-term investment results to help end-investors reach their financial goals. We use our expertise in asset allocation, investment selection and portfolio construction to create world-class investment strategies leveraging the full resources of Morningstar. The group specializes in multi-asset investing, using building blocks in equities, fixed income and alternative investments to construct robust portfolios. Through our investment offerings, we serve financial advisers and institutions, and the investors that they serve.

Role Summary:

The Quantitative Developer will join the Systematic Strategies team in the Research & Investment group. This experienced professional will build scalable research infrastructure used by portfolio managers and quantitative researchers to develop, back test, and deploy multi asset models.
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 investment strategies.
This role is ideal for someone who thrives at the intersection of data engineering, cloud architecture, and financial systems.

Key Responsibilities:

The successful candidate will

• Design and maintain scalable data pipelines for market, fundamental, and alternative datasets using Python, PySpark, FastAPI and AWS
• Build and support investment data platforms, maintain databases and research datasets.
• Integrate and automate data retrieval from internal/external providers such as FactSet, Morningstar, Axioma, and other third-party sources
• Collaborate with quantitative researchers and portfolio managers to support enhancement of production workflows
• Automate operational processes through workflow orchestration, CI/CD, and Infrastructure-as-Code practices
• Exposure to streaming data services and event-driven architectures used for real-time data ingestion, processing, and distribution
• Evaluate and leverage AI/ML and Generative AI technologies to enhance analytics and operational efficiency

Requirements:

• Strong expertise in Python, PySpark, and distributed data processing
• Experience with Airflow, EMR, AWS Step Functions, Docker, Git, CI/CD, Terraform, and CloudFormation
• Strong SQL skills and experience with Redshit, Athena, DataLake and Parquet
• Experience of building AI agents & agentic workflow is desirable and will be considered a strong plus
• Experience with handling financial data from vendors such as FactSet, Bloomberg, Morningstar, or Compustat
• Exposure to portfolio analytics, risk modelling, performance attribution, and platforms such as Axioma, MSCI Barra, Bloomberg PORT, or similar solutions

Required Technical Skills

• Advanced SQL, Python and PySpark
• Experience with creating Data Pipelines
• Exposure to cloud-based services, AWS (preferred)
• Exposure to AI-powered productivity and development tools such as ChatGPT, Microsoft Copilot, and GitHub Copilot

Preferred Qualifications

• Bachelor’s or master’s degree in engineering, Computer Science, Finance, Mathematics, Statistics, or a related quantitative discipline
• 2+ years of experience in Quantitative Engineering, Data Engineering, or Platform Engineering within investment management or financial services

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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