Senior Quantitative Developer
Morningstar Open At a glance
Senior Data Science role at Morningstar. Mumbai · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain PySpark pipelines that bring financial and alternative data into quantitative research workflows
- Run AWS infrastructure supporting research and production, including compute, storage and data services
- Work with researchers and portfolio managers to prepare data and put quantitative strategies into operation
- Create dashboards and internal tools that show pipeline health, system performance and data quality
- Maintain backtesting and simulation platforms, improve system efficiency, and automate operational work and CI/CD
What you bring
- At least five years in a quantitative, data or platform engineering role handling investment data
- A bachelor's or master's degree in a quantitative, finance-related or engineering discipline
- Strong Python and PySpark skills, including experience processing distributed data
- Practical AWS experience, plus knowledge of financial datasets and data architecture
Who this fits
This senior role suits someone who can connect data engineering and cloud systems with quantitative research and portfolio work. The listed locations are Navi Mumbai or Delhi, with a UK shift. The hybrid schedule calls for four office days per week in most locations.
From the employer
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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