Lead Data Engineer- Vice President - Data Engineering

Morgan Stanley · Mumbai · 10–18 yrs experience · Posted 2026-06-10

Tech stack: Python, SQL

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About the role

Responsibilities:
- Drive the adoption and integration of GenAI, LLMs, and modern AI/ML techniques for ETL automation, data enrichment, reporting commentary, and intelligent data distribution across the enterprise.
- Provide technical leadership and mentorship to a high-performing team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
- Collaborate with business stakeholders, technology partners, and cross-functional teams to define data strategy, requirements, and deliverables aligned with organizational goals.
- Champion modern SDLC practices, including automated testing, CI/CD, and agile methodologies, to ensure high-quality, scalable, and maintainable solutions.
- Drive automation, data quality, and best practices across all data engineering processes and solutions.
- Ensure robust data governance, security, and compliance throughout the data lifecycle.
- Manage stakeholder relationships, communicate project status, and proactively address risks and challenges.
- Champion the adoption of new technologies and methodologies to enhance data capabilities and business value.
Qualifications:
- 10-18 years of experience in data engineering, data architecture, or related roles, with a proven track record of delivering enterprise-level solutions.
- At least 6 years' relevant experience would generally be expected to find the skills required for this role.
- Deep expertise in SQL, data modeling, ETL, and building scalable data pipelines.
- Mandatory: Strong domain and functional knowledge in finance, investment banking, or related industries.
- Demonstrated experience leveraging GenAI, Copilot, or AI-based solutions for enterprise data, reporting, and analytics use cases, including design, code completion, review, and automated test case generation.
- Strong hands-on experience with cloud data platforms (preferably Snowflake) and modern data engineering tools.
- Proven ability to lead, motivate, and develop high-performing teams.
- Excellent problem-solving, analytical, and communication skills.
- Experience managing stakeholder relationships and delivering complex projects in a global environment.
- Strong understanding of modern SDLC, agile delivery, and innovation in data engineering.
- Experience with automated testing and testing frameworks.
- Additional Skills ():
- Familiarity with Power BI, Apache Airflow, and OLAP tools.
- Experience with Python, Shell scripting, and workflow automation.
- Exposure to regulatory and financial reporting requirements.
- Demonstrated track record of driving innovation and GenAI adoption in data engineering projects.
- Passion for continuous learning, business impact, and solution-oriented leadership.

Qualifications

- 10-18 years of experience in data engineering, data architecture, or related roles, with a proven track record of delivering enterprise-level solutions.
- At least 6 years' relevant experience would generally be expected to find the skills required for this role.
- Deep expertise in SQL, data modeling, ETL, and building scalable data pipelines.
- Mandatory: Strong domain and functional knowledge in finance, investment banking, or related industries.
- Demonstrated experience leveraging GenAI, Copilot, or AI-based solutions for enterprise data, reporting, and analytics use cases, including design, code completion, review, and automated test case generation.
- Strong hands-on experience with cloud data platforms (preferably Snowflake) and modern data engineering tools.
- Proven ability to lead, motivate, and develop high-performing teams.
- Excellent problem-solving, analytical, and communication skills.
- Experience managing stakeholder relationships and delivering complex projects in a global environment.
- Strong understanding of modern SDLC, agile delivery, and innovation in data engineering.
- Experience with automated testing and testing frameworks.
- Additional Skills ():
- Familiarity with Power BI, Apache Airflow, and OLAP tools.
- Experience with Python, Shell scripting, and workflow automation.
- Exposure to regulatory and financial reporting requirements.
- Demonstrated track record of driving innovation and GenAI adoption in data engineering projects.
- Passion for continuous learning, business impact, and solution-oriented leadership.

Responsibilities

- Drive the adoption and integration of GenAI, LLMs, and modern AI/ML techniques for ETL automation, data enrichment, reporting commentary, and intelligent data distribution across the enterprise.
- Provide technical leadership and mentorship to a high-performing team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
- Collaborate with business stakeholders, technology partners, and cross-functional teams to define data strategy, requirements, and deliverables aligned with organizational goals.
- Champion modern SDLC practices, including automated testing, CI/CD, and agile methodologies, to ensure high-quality, scalable, and maintainable solutions.
- Drive automation, data quality, and best practices across all data engineering processes and solutions.
- Ensure robust data governance, security, and compliance throughout the data lifecycle.
- Manage stakeholder relationships, communicate project status, and proactively address risks and challenges.
- Champion the adoption of new technologies and methodologies to enhance data capabilities and business value.

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