Middle Office Data Engineering and Quality_Vice President_Product Support & Services IM

Morgan Stanley · Mumbai · 10+ yrs experience · Posted 2026-06-10

Tech stack: Python

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About the role

Responsibilities:
- Own data quality framework for Reference Data and Core Transaction Processing Data ensuring accurate and timely capture of portfolio level accounting data across key data sets.
- Strong people management skills to ensure smooth functioning of the DEQ team
- 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 and/or resolution, and escalation.
- Provide high-touch leadership and accountability of complex and sensitive data sets, including those powering performance, reporting, and regulatory processes.
- Ensure proactive, high-quality outcomes for clients and investment teams, and other data consumers of MSIM's books and records.
- Build and implement data-driven platforms, dashboards, and automated controls.
- Identify & propose AI solutions for process transformation & data insights for more precise business decision making & optimization
- Maintain supervisory oversight on team activities, risk, department and firm initiatives
- Deliver and manage KPI dashboards, trend analysis, and exception reporting to senior stakeholders and data governance forums.
Qualifications:
- 10+ years of experience in Investment Operations, Data Management, or Middle Office data and analytics at a global asset manager or financial services firm.
- A strong understanding of data quality principles, including accuracy, completeness, consistency, and timeliness. Experienced in establishing Data quality forums with key stakeholders & built reporting dashboards.
- Proficient in data validation, data cleansing, and data governance.
- Experience in investment management across pooled vehicles, SMAs, and institutional mandates.
- Demonstrated understanding of investment and trade lifecycle, with focus on portfolio tax lot level recordkeeping inputs (reference data, marker trades, prices, corporate actions, accruals, amortization, etc.).
- Familiarity with a full range of financial products, including public and private instruments, and asset classes (e.g., equity, fixed, derivatives, loans).
- AI savvy and experienced with leading process improvements and transformation. Change management experienced. Experienced in leveraging python & LLM libraries for faster adoption process automation concepts, experienced in leveraging API for data transfers, experienced in NLP / OCR solution implementation. Alteryx workflow experience for data conversion and data / rule optimization & conversion. Experienced in cloud strategy especially from data use and transfer perspective. Databricks AI module experience will be value add
- Lead & supported solutions which involved, client & trade lifecycle automation involving AI solutions
- Demonstrated leading / supporting key change the bank initiatives and ability to articulate current and target operating models by leveraging tools and technologies
- Experience with data quality tools, dashboards, APIs to support real-time data validation and exception handling.
- Strong technology (pro dev and low code) experience, with a track record of developing data-driven platforms, dashboards, and automated controls
- Strong analysis and analytical skills to perform root cause analysis, data lineage, exception handling, and process optimization.
- Demonstrated ability to synthesis data for MI reporting on key performance indicators and other portfolio accounting data as inputs into middle office data health and identifying areas for operational optimization.
- Experience with Data Quality platforms (e.g. Pan DQ, Informatica, PEGA Exception Management) & Business Intelligence Tools (e.g. Alteryx, PowerBI, etc)
- Experienced to lead & run with multiple workstreams associated to Data quality which involves multiple senior stakeholders spread across operations, technology & Data functions. To be able to drive Data quality process and outcomes.

Qualifications

- 10+ years of experience in Investment Operations, Data Management, or Middle Office data and analytics at a global asset manager or financial services firm.
- A strong understanding of data quality principles, including accuracy, completeness, consistency, and timeliness.
- Experienced in establishing Data quality forums with key stakeholders & built reporting dashboards.
- Proficient in data validation, data cleansing, and data governance.
- Experience in investment management across pooled vehicles, SMAs, and institutional mandates.
- Demonstrated understanding of investment and trade lifecycle, with focus on portfolio tax lot level recordkeeping inputs (reference data, marker trades, prices, corporate actions, accruals, amortization, etc.).
- Familiarity with a full range of financial products, including public and private instruments, and asset classes (e.g., equity, fixed, derivatives, loans).
- AI savvy and experienced with leading process improvements and transformation.
- Change management experienced.
- Experienced in leveraging python & LLM libraries for faster adoption process automation concepts, experienced in leveraging API for data transfers, experienced in NLP / OCR solution implementation.
- Alteryx workflow experience for data conversion and data / rule optimization & conversion.
- Experienced in cloud strategy especially from data use and transfer perspective.
- Databricks AI module experience will be value add
- Lead & supported solutions which involved, client & trade lifecycle automation involving AI solutions
- Demonstrated leading / supporting key change the bank initiatives and ability to articulate current and target operating models by leveraging tools and technologies
- Experience with data quality tools, dashboards, APIs to support real-time data validation and exception handling.
- Strong technology (pro dev and low code) experience, with a track record of developing data-driven platforms, dashboards, and automated controls
- Strong analysis and analytical skills to perform root cause analysis, data lineage, exception handling, and process optimization.
- Demonstrated ability to synthesis data for MI reporting on key performance indicators and other portfolio accounting data as inputs into middle office data health and identifying areas for operational optimization.
- Experience with Data Quality platforms (e.g. Pan DQ, Informatica, PEGA Exception Management) & Business Intelligence Tools (e.g. Alteryx, PowerBI, etc)
- Experienced to lead & run with multiple workstreams associated to Data quality which involves multiple senior stakeholders spread across operations, technology & Data functions.
- To be able to drive Data quality process and outcomes.

Responsibilities

- Own data quality framework for Reference Data and Core Transaction Processing Data ensuring accurate and timely capture of portfolio level accounting data across key data sets.
- Strong people management skills to ensure smooth functioning of the DEQ team
- 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 and/or resolution, and escalation.
- Provide high-touch leadership and accountability of complex and sensitive data sets, including those powering performance, reporting, and regulatory processes.
- Ensure proactive, high-quality outcomes for clients and investment teams, and other data consumers of MSIM's books and records.
- Build and implement data-driven platforms, dashboards, and automated controls.
- Identify & propose AI solutions for process transformation & data insights for more precise business decision making & optimization
- Maintain supervisory oversight on team activities, risk, department and firm initiatives
- Deliver and manage KPI dashboards, trend analysis, and exception reporting to senior stakeholders and data governance forums.

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