Senior Data Engineer - Vice President

Morgan StanleyNew York, NY
1d$150,000 - $210,000

About The Position

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Lead Data Engineering position at Vice President level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques. 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. Morgan Stanley is a leading global financial services firm, providing investment banking, securities, wealth management, and investment management services in over 40 countries. The firm is recognized for its commitment to innovation, integrity, and delivering value to clients, shareholders, and communities worldwide. About Finance Technology: Finance Technology at Morgan Stanley delivers innovative solutions for regulatory and financial reporting, general ledger, P&L calculations, and analytics. The team leverages advanced data platforms and modern engineering practices to support the firm’s finance division, ensuring accuracy, compliance, and strategic business insights.

Requirements

  • 10+ years of experience in data engineering, data architecture, or related roles, with a proven track record of delivering enterprise-level solutions.
  • Deep expertise in SQL, data modelling, ETL, and building scalable data pipelines.
  • Strong hands-on experience with cloud data platforms (preferably Snowflake) and modern data engineering tools.
  • Strong hands-on experience with Python, Shell scripting, and workflow automation.
  • Demonstrated experience leveraging GenAI, LLMs, or AI/ML solutions for enterprise data, reporting, and analytics use cases.
  • Proven ability to lead, motivate, and develop high-performing teams.
  • Strong domain and functional knowledge in finance, investment banking, or related industries.
  • 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.

Nice To Haves

  • Familiarity with Power BI, Apache Airflow, and OLAP tools.
  • 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

  • Lead the design, development, and implementation of enterprise-scale data warehouse, reporting, and analytics solutions, preferably on cloud platforms such as Snowflake.
  • 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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