Morningstar India has been a Great Place to Work-certified company for the past eight consecutive years.
Team: Sustainalytics is Morningstar’s ESG and Climate intelligence business and a core provider of high-precision sustainability data to global capital markets. The team produces structured and unstructured ESG and Climate datasets at scale, delivers risk models and ratings, and supports products used by leading institutional investors worldwide. The business is entering the next phase of operating model and technology transformation. India is central to that strategy, with Mumbai positioned to become the primary global hub for large-scale data production, automation-led delivery, governance and operational excellence.
This role will have direct influence on the scalability, economics, resilience and long-term competitiveness of Sustainalytics’ future-state data platform.
Role Purpose: Lead a large-scale ESG and Climate data operations organization in Mumbai responsible for delivering accurate, reliable and timely data that powers Sustainalytics’ research, analytics, client products and delivery commitments.
This role is being created to lead the next phase of global transformation by:
Consolidating international production capabilities into Mumbai
Deploying AI-native automation at scale
Re-engineering workflows from first principles
Lowering unit cost of production materially
Improving quality, speed and resilience
Building a world-class operations capability for the future
In addition to operational transformation, this role will serve as a key leader for data quality excellence across Sustainalytics. The role will define and strengthen enterprise-wide quality frameworks, controls, governance standards and client-centric quality measurement capabilities to ensure Sustainalytics continues to operate at the standards expected of a global financial data provider.
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The datasets produced by this function are consumed directly by asset managers, asset owners, banks and insurers for investment decisions, regulatory filings and client reporting. As a result, the operation must meet the same standards of precision, timeliness and control expected of any mission-critical financial data provider.
Key responsibilities:
Global Data Production Leadership
Lead end-to-end sourcing, ingestion, extraction, normalization, QA, enrichment and classification workflows.
Manage periodic refresh cycles and high-volume production runs.
Ensure outputs consistently meet client, research and product requirements.
Build a predictable, industrial-scale production engine across a 300–400 FTE environment.
Global Consolidation into Mumbai
Lead consolidation of global data operations into Mumbai, including migration of activities currently performed in other locations, including Romania.
Own transition planning, workforce movement, operating model redesign and stakeholder communication.
Ensure continuity of delivery, quality and client commitments throughout migration.
Build Mumbai into the flagship global delivery hub for this function.
AI Automation & Productivity Transformation
Own design and deployment of AI-driven, rules-based and agentic automation across production workflows.
Reduce manual intervention significantly within 12–18 months.
Identify use cases for LLMs, workflow agents and intelligent tooling that augment or replace manual work.
Deliver measurable productivity gains and sustained reduction in total production cost.
Process Re-engineering & Operational Excellence
Apply engineering-led thinking to deconstruct workflows end-to-end.
Identify structural inefficiencies and redesign processes from first principles rather than optimize legacy constraints.
Improve throughput, cycle times, scalability and control environment.
Build a culture of continuous improvement and high accountability.
Financial Data Management & Governance
Oversee security master data, entity hierarchies, product / instrument classification systems and reference data ecosystems.
Ensure consistency, lineage, governance and auditability across the data value chain.
Maintain standards expected by institutional investors and regulated financial organizations.
Executive Stakeholder Partnership
Partner closely with Product, Technology, Research, Client Management and global leadership teams.
Operate effectively in a complex, evolving product environment with senior stakeholders across regions.
Provide clear leadership communication, structured updates and decisive execution.
Quality Strategy, Governance & Client Experience
Lead Sustainalytics-wide quality initiatives and establish methods, governance processes and measurement frameworks that provide broad visibility into data quality performance and client experience outcomes.
Partner closely with Product, Research, Data, Technology and Client Service leadership teams to define and execute enterprise quality strategies aligned to client expectations and business priorities.
Define the long-term quality roadmap, including quality controls, monitoring frameworks, scorecards, benchmarking mechanisms and audit methodologies across the data lifecycle.
Build ongoing client quality scorecards and operational quality reporting mechanisms that provide actionable insight to leadership teams.
Monitor quality governance activities, operational controls, root-cause analysis outcomes, audit findings and remediation programs to ensure sustainable quality improvement.
In partnership with data and operations leaders, identify systemic quality gaps and drive structured execution plans anchored in best practices and measurable outcomes.
Collaborate with Morningstar quality leaders globally to share best practices, standardize quality principles and participate in enterprise-wide quality transformation initiatives.
Success measures
High-fidelity ESG & Climate data delivered consistently at scale
Successful migration of global operations into Mumbai with no material disruption
Significant reduction in unit cost of production
Meaningful reduction in manual workflows within 12–18 months
Improved cycle times and throughput productivity
Higher automation coverage across production processes
Strong SLA adherence and lower exception rates
Reduced client-impacting incidents and faster resolution times
Strong leadership bench and sustainable operating model in Mumbai
Improvement in enterprise data quality and client quality satisfaction metrics
Reduction in repeat quality incidents and systemic data exceptions
Increased audit readiness, governance maturity and quality control coverage
Adoption of enterprise quality scorecards and benchmarking frameworks
Strong root-cause remediation discipline and sustainable quality improvement outcomes
Candidate profile
20+ years leading large-scale data operations in financial services, analytics, information services or adjacent industries.
Proven success leading 300+ FTE operations with strong governance and performance discipline.
Demonstrated experience leading transformation, consolidation or operating model redesign programs.
Deep familiarity with financial data management, including security master, entity structures, classifications and reference data infrastructure.
Strong understanding of how investment managers, banks, insurers and institutional clients consume critical data.
Hands-on a
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