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Middle Office Data Engineering and Quality_Director_Core Services

Morgan Stanley

Middle Office Data Engineering and Quality_Director_Core Services

Morgan Stanley

Mumbai, India

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

401(k) matching

Flexible work arrangements

Parental leave

Professional development budget

Team events and activities

Comprehensive health, dental, and vision insurance

Flexible Hours

Parental Leave

Learning

Healthcare

Required Skills

JavaScript

Python

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We are seeking a strategic, data-driven Middle Office Data Engineering and Quality - Director to lead the build and evolve a best-in-class Data Quality and Engineering function in India.
This role sits within the Public Markets Middle Office and is pivotal to ensuring data accuracy, transparency, and operational readiness across the firm's transaction lifecycle in support of MSIM clients, investment teams and traders, and other essential uses of this data. The successful candidate will be an innovative data science professional, who will develop quality control checks and insights to mitigate data quality issues, identify anomalies in data for research, and continuously improve upon the process to expand across a complex data set. This position will partner closely with the Middle Office functional teams to establish and evolve the cross-platform data quality framework and controls for transaction lifecycle data to support efficient trade processing
In the Operations division, we partner with business units across the Firm to support financial transactions, devise and implement effective controls and develop client relationships. This is a Team Supervisor position at Director level within the Core Services, which is responsible for performing and managing product-agnostic and centralized operational services across several businesses and products.
Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you'll do in the role:

  • Data Quality Framework & Controls: Own data quality framework for cross platform transaction lifecycle management data.
  • Lead cross platform alignment for integrated controls and monitoring and ability to navigate business matters.
  • Develop, maintain, and operationalize data quality controls across the transaction lifecycle
  • Align with enterprise data strategy/governance initiatives to ensure implementation within Middle Office.
  • Maintain robust data validation and exception monitoring processes to ensure cross platform completeness, accuracy, and timeliness cross.
  • 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.
  • Document business requirements and use cases for data-related projects.
  • 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, including managing change control process and participate in user acceptance testing (UAT) activities as required.
  • Deliver and manage KPI dashboards, trend analysis, and exception reporting to senior stakeholders and data governance forums BS/BA degree required.

What you'll bring to the role:

  • 7+ 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 timelines.
  • 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 cross platform data sets and outcomes (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 experience with leading process improvements and transformation.
  • 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. PanDQ, Informatica, PEGA Exception Management) & Business Intelligence Tools (e.g. Alteryx, PowerBI, etc...)

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients - helping them reach their goals. We do it in a way that's differentiated - and we've done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren't just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you'll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There's also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Morgan Stanley is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of the global communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.

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About Morgan Stanley

Morgan Stanley

A financial services company that offers securities, asset management, and credit services.

10,001+

Employees

New York

Headquarters

Reviews

3.5

4 reviews

Work Life Balance

3.0

Compensation

2.5

Culture

3.2

Career

3.0

Management

3.0

35%

Recommend to a Friend

Pros

Skills evaluation through business plans and projects

Direct access to senior leadership interviews

Conversational interview format

Cons

Automated resume screening system issues

Focus on formatting over qualifications

Compensation concerns and salary expectations

Salary Ranges

11,766 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analyst

1,682 reports

$114,371

total / year

Base

$96,366

Stock

-

Bonus

$18,005

$77,808

$170,800

Interview Experience

6 interviews

Difficulty

3.0

/ 5

Duration

21-35 weeks

Experience

Positive 16%

Neutral 84%

Negative 0%

Interview Process

1

Initial screening (HR/HireVue)

2

Technical rounds

3

Manager/Senior leadership interviews

4

Final round/Superday

Common Questions

Technical knowledge assessment

Behavioral questions

Role-specific scenarios

Leadership and teamwork examples