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Morgan Stanley

Manages investments and advises on financing

Middle Office Data Engineering and Quality Vice President - Product Support & Services IM

Data ManagementConfirmed live in the last 24 hours
Full-timeMumbai, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 10 years of experience in investment operations, data management, or middle-office data and analytics at a global asset manager or financial services firm.
  • Strong understanding of data quality principles, including accuracy, completeness, consistency, and timeliness, with experience establishing data quality forums and building reporting dashboards.
  • Proficiency in data validation, data cleansing, and data governance.
  • Experience in investment management across pooled vehicles, separately managed accounts, and institutional mandates.
  • Understanding of the investment and trade lifecycle, particularly portfolio tax-lot-level recordkeeping inputs such as reference data, marker trades, prices, corporate actions, accruals, and amortization.
  • Familiarity with a full range of financial products, including public and private instruments and asset classes such as equity, fixed income, derivatives, and loans.
  • AI knowledge and experience leading process improvements and transformation, including change management; experience using Python and large language model libraries for automation, APIs for data transfers, natural language processing and optical character recognition solutions, Alteryx workflows for data conversion and data or rule optimization, and cloud strategy from a data-use and transfer perspective. Databricks AI module experience is an added value.
  • Experience leading or supporting solutions involving client and trade lifecycle automation using AI solutions.
  • Experience leading or supporting key change-the-bank initiatives and articulating current and target operating models using tools and technologies.
  • Experience with data quality tools, dashboards, and APIs for real-time data validation and exception handling.
  • Strong technology experience, including pro-code and low-code tools, and a track record of developing data-driven platforms, dashboards, and automated controls.
  • Strong analytical skills for root-cause analysis, data lineage, exception handling, and process optimization.
  • Ability to synthesize data for management information reporting on key performance indicators and portfolio accounting data, to inform middle-office data health and identify operational optimization opportunities.
  • Experience with data quality platforms such as Pan DQ, Informatica, or PEGA Exception Management, and business intelligence tools such as Alteryx or Power BI.
  • Experience leading and managing multiple data-quality workstreams with senior stakeholders across operations, technology, and data functions, and driving data-quality processes and outcomes.

Responsibilities

  • Own the data quality framework for reference data and core transaction-processing data, ensuring accurate and timely capture of portfolio-level accounting data across key datasets.
  • Develop, maintain, and operationalize data quality controls on portfolio transaction, holding, and valuation datasets.
  • Maintain robust data validation and exception-monitoring processes to ensure completeness, accuracy, and timeliness of portfolio accounting data.
  • Support research and triage of data exceptions, enabling scalable issue identification, mitigation or resolution, and escalation.
  • Provide leadership and accountability for complex and sensitive datasets, including those powering performance, reporting, and regulatory processes.
  • Ensure high-quality outcomes for clients, investment teams, and other consumers of MSIM’s books and records.
  • Build and implement data-driven platforms, dashboards, and automated controls.
  • Identify and propose AI solutions for process transformation and data insights to improve business decision-making and optimization.
  • Maintain supervisory oversight of team activities, risk, and department and firm initiatives.
  • Deliver and manage KPI dashboards, trend analysis, and exception reporting for senior stakeholders and data governance forums.
  • Lead the build and evolution of the Data Engineering and Quality function in India.
  • Partner with operations, business, technology, and data office teams to establish and evolve the data quality framework and controls supporting timely, high-quality investment and accounting books and records.

About the Company

Morgan Stanley

Manages investments and advises on financing

Morgan Stanley advises companies and governments on financing and manages investments for institutions, individuals, and families. Its business combines investment banking and securities trading with wealth management and an asset-management division that invests across public and private markets. Bankers arrange stock and bond sales and advise on mergers, while advisors build client portfolios and fund managers invest money under agreed strategies. The firm earns advisory and management fees, trading revenue, commissions, and interest from lending and deposits. Morgan Stanley was founded as a partnership in 1935 and is headquartered in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1935

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