Top Data & Analytics Jobs in Mumbai, MH

Leads and mentors a data engineering team responsible for enterprise data pipelines, models, governance, quality, and platform reliability. Defines the data engineering roadmap, guides cloud architecture decisions, and partners with business and technology leaders. Oversees scalable ETL/ELT workflows using modern data technologies, optimizes performance and costs, and advances AI-enabled analytics capabilities. Builds team effectiveness through hiring, coaching, retention, and performance development.

Supports structured finance analytics by automating data analysis, document downloads, data storage, reporting, and workflow optimization. Combines data from multiple sources, develops analytics using databases and cloud storage, and contributes to ratings, research, surveillance, and criteria development. The role requires accuracy, organization, teamwork, process improvement, and compliance with company policies.

Entry-level apprentice supporting data collection, verification, research, and quality control across financial and investment datasets. The role involves responding to internal and client queries, following data methodologies, resolving data issues with cross-functional teams, and meeting productivity, quality, and deadline standards. Structured training is provided during the 6–12 month apprenticeship, with possible early morning, afternoon, evening, and night shifts.

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.

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.