Top Tech Jobs & Startup Jobs in Mumbai

Build and maintain enterprise data pipelines, ELT processes, data models, reporting structures, and analytics solutions. Cleanse, deduplicate, normalize, and govern data across warehouses and data lakes, including Snowflake. Develop dashboards and business intelligence models, ensure data quality and compliance, apply business logic, and collaborate with technical and non-technical stakeholders to deliver accurate insights and improve operational performance.

Entry-level apprentice supporting data collection, verification, quality assurance, and query resolution across investment and financial datasets. The role includes structured training in investment management, Excel, SQL, and Power BI, collaboration with internal and external stakeholders, and work across rotating shifts in the Mumbai office.

Develop and maintain scalable full-stack features for Morningstar Direct using Java, modern JavaScript/TypeScript frameworks, cloud infrastructure, automated testing, and CI/CD. Own delivery from design through production support, improve reliability and observability, troubleshoot frontend and backend issues, mentor engineers, and apply responsible AI-assisted development and testing practices.

Develop and maintain automated testing frameworks for Morningstar Indexes applications. Define automation strategies, create test cases, and implement coverage across UI, backend integration, databases, and web services. Integrate automation with CI/CD and cloud testing environments, document defects, assist with root-cause analysis, and collaborate with technology and business teams throughout development and operations.

Leads product strategy and execution for a retirement managed-accounts platform. Acts as Product Owner for an Agile scrum team, writes user stories, prioritizes features, analyzes product data, manages roadmaps, and collaborates with engineering, design, data, and clients. The role requires API and integration knowledge, generative AI fluency, regulated financial-services judgment, and oversight of client and user support operations.

Develop scalable full-stack web applications and APIs using C#, .NET, AWS, SQL Server, and PostgreSQL. Responsibilities include designing reliable products, writing unit tests, practicing continuous integration and test-driven development, resolving production issues, improving system instrumentation, collaborating with product managers, following Agile practices, and applying secure, scalable software architecture. The role is based in Mumbai with a hybrid schedule.

Build scalable data pipelines and investment data platforms supporting quantitative research, backtesting, and deployment of multi-asset investment models. Responsibilities include processing market and fundamental data, integrating third-party providers, maintaining databases and research datasets, automating workflows with CI/CD and infrastructure-as-code, and supporting cloud-based and event-driven architectures. The role collaborates closely with quantitative researchers and portfolio managers and explores AI/ML and generative AI applications.

Product Manager responsible for integrations, working in Morningstar’s hybrid Delhi environment. The provided description contains no specific responsibilities, qualifications, technologies, salary, travel, or clearance details.

Leads and mentors a software engineering team while serving as its primary engineering voice. Designs, builds, scales, and maintains distributed systems for website crawling and information extraction using cloud technologies, microservices, and modern development practices. Partners with Product, UX, and Data Science, drives projects across the full software development lifecycle, improves technical standards, reduces technical debt, and promotes AI-assisted development practices.

Leads Morningstar’s Investment and Time Series team, overseeing quantitative methodology, financial analytics, time-sensitive investigations, and larger research initiatives. Serves as the technical methodology authority, reviews logic, ensures quality, coordinates with Product, Data, Technology, and market stakeholders, improves documentation and AI-assisted validation, and mentors quantitative researchers. Requires broad expertise in investment analytics, time series, asset allocation, modern data tools, and team leadership.