Senior Manager of Software Engineering

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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