Senior Manager of Software Engineering
Morningstar · Mumbai
Senior Technology Manager, Indexes Technology
The Team
Morningstar Indexes combines deep expertise in equity research, manager research, asset allocation, portfolio construction, and modern technology to create investment solutions that support market monitoring, benchmarking, asset allocation, and client delivery. The team offers a broad suite of global equity, bond, commodity, and asset allocation indexes, and is actively modernizing critical platforms to improve resilience, scalability, automation, data quality, and delivery speed.
The Role
We are looking for a Senior Technology Manager to lead engineering delivery for strategic platform modernization initiatives within Morningstar Indexes. This role will help modernize critical applications, strengthen cloud-based architecture, improve QA/QC and data-quality controls, and build reusable engineering patterns for future technology transformation initiatives. The ideal candidate is a hands-on technology leader who can operate across architecture, delivery, engineering management, stakeholder alignment, and AI-enabled software development. This person will partner closely with product, operations, research, QA, architecture, security, and global engineering teams to deliver high-quality outcomes with strong execution discipline and minimal client disruption.
Responsibilities
- Lead one or more engineering workstreams focused on platform modernization, cloud adoption, application resiliency, QA/QC modernization, data-quality automation, and operational readiness.
- Own technical execution across engineering squads and ensure delivery plans are aligned to business priorities, architecture decisions, product needs, security requirements, and acceptance criteria.
- Act as a software and architecture steward by driving sound engineering practices, scalable design, code quality, observability, resiliency, performance, security, and responsible technical-debt management.
- Manage and develop a team of software engineers, QA engineers, automation engineers, and scrum master(s), building a high-accountability culture focused on ownership, predictability, quality, and continuous improvement.
- Partner with product owners, business analysts, operations, QA/QC, architecture, security, infrastructure, and global stakeholders to clarify requirements, remove blockers, manage risks, and support release readiness.
- Drive AI-assisted engineering practices across the team, including responsible use of AI tools for code generation, code review, test creation, documentation, knowledge capture, technical analysis, and developer productivity.
- Establish repeatable engineering playbooks, validation patterns, automation frameworks, and standards that can be reused across products and future modernization initiatives.
- Promote transparent communication through clear status reporting, milestone tracking, decision logs, risk management, and stakeholder-ready updates for leadership and delivery teams.
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or equivalent practical experience.
- 12+ years of experience in progressively senior technology roles, with strong experience leading complex software delivery, platform modernization, or migration programs.
- 5+ years of experience managing engineering teams using Agile delivery practices, with demonstrated ability to improve execution discipline, predictability, quality, and team ownership.
- Strong hands-on technical background in Java, Spring Boot, APIs, microservices, SQL databases, cloud-native application design, and secure software engineering practices.
- Practical experience with AWS or comparable cloud platforms, including services such as ECS, Lambda, S3, RDS/PostgreSQL, Glue, Step Functions, IAM, KMS, monitoring, and CI/CD automation.
- AI-based development experience is required, including hands-on use of AI coding assistants or generative AI tools to improve code quality, test coverage, documentation, analysis, and engineering productivity.
- Ability to define responsible AI usage patterns for engineering teams, including prompt discipline, human review, validation controls, security awareness, and prevention of unverified or unsafe AI outputs.
- Strong understanding of application architecture, design patterns, data pipelines, batch and event-driven processing, integration patterns, observability, resiliency, and production support models.
- Experience leading application modernization, platform transformation, data modernization, or cloud adoption initiatives with clear validation, quality, and business acceptance criteria.
- Strong stakeholder management skills, with the ability to work across engineering, product, operations, QA, architecture, security, and geographically distributed teams.
- Good understanding of investment data, indexes, market data, research operations, or financial services platforms is preferred.
- Self-directed, detail-oriented, and comfortable operating in a rapidly changing global environment with high delivery urgency and strong quality expectations.
Must Have
- 12+ years of software engineering or technology leadership experience, including ownership of complex enterprise delivery, platform modernization, or large-scale technology transformation initiatives.
- Proven experience managing engineering teams and driving Agile execution with strong focus on delivery predictability, code quality, accountability, and operational readiness.
- Hands-on technical depth in Java, Spring Boot, APIs, microservices, SQL databases, cloud-native design, CI/CD, secure engineering, and production-grade application delivery.
- Practical cloud experience, preferably AWS, including modern application deployment, infrastructure integration, monitoring, reliability, security, and automation practices.
- Mandatory experience with AI-based development, including use of AI coding assistants or generative AI tools for code generation, code review, test automation, documentation, technical analysis, or productivity improvement.
- Ability to apply responsible AI practices, including prompt discipline, developer review, validation controls, security awareness, and prevention of unverified AI-generated output.
- Experience leading or contributing to application modernization, data modernization, cloud adoption, platform transformation, or technology simplification initiatives.
- Strong stakeholder management and communication skills, with ability to work across product, operations, research, QA, architecture, security, and geographically distributed engineering teams.
Good to Have
- Exposure to financial services, investment data, indexes, market data, research operations, benchmarks, or asset management platforms.
- Experience with AWS services such as ECS, Lambda, S3, RDS/PostgreSQL, Glue, Step Functions, IAM, KMS, CloudWatch, and event-driven or batch processing patterns.
- Experience improving QA/QC operating models, automation frameworks, regression testing, validation workflows, and data-quality controls for critical business platforms.
- Experience creating reusable engineering playbooks, delivery plans, runbooks, decision logs, risk registers, validation approaches, or executive-ready status reporting.
- Familiarity with data pipelines, ETL/ELT workflows, data mastering, observability, resiliency engineering, and production support for data-intensive platforms.
- Ability to coach teams on AI adoption, engineering productivity, modern development practices, and continuous improvement across distributed teams.
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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