Vice President - Cloud Data & Analytics Engineer - Investment Management IT
Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in more than 41 countries, the Firm's employees serve clients worldwide including corporations, governments, institutions and individuals. For further information about Morgan Stanley, please visit www.morganstanley.com.
Investment Mangement IT (IMIT)
IMIT provides industry-leading strategies and solutions to enable business growth and deliver “best in class” functions to Morgan Stanley’s Investment Management business.
The position is for a Cloud Data & Analytics Engineer within the Data Strategy team at MSIM. The candidate is expected to work on design and development of end to end Cloud based solutions with heavy focus on analytics and data, with good understanding of underlying cloud infrastructure
Skill Set: Data Engineering / Python / Spark / Cloud
Independently lead & manage execution of data engineering projects
Engineer complete technical solutions to solve concrete business challenges in the areas of Data management, Business Intelligence and self-service analytics
Collect functional and non-functional requirements, consider technical environments, business constraints and enterprise organizations
Support our clients in executing their Big Data strategies by designing and building operational data platforms: ETL pipelines, data anonymization pipelines, data lakes, near real-time streaming data hubs, web services, training and scoring machine learning models.
Troubleshoot and quality check work done by team members
Collaborate closely with partners, strategy consultants and data scientists in a flat and agile organization where personal initiative is highly valued
Share data engineering knowledge by giving technical trainings
Communicate and interact with clients at the executive level
Guide and mentor team members
The candidates should have strong capabilities in data engineering along with a proven expertise in team management.
Candidates should also be proficient in project and senior stakeholder management.
A broad practice in multiple software engineering fields
Experience on managing & leading data engineering / warehousing projects
Must have experience on Python, SQL and Distributed programming languages, preferable Spark
Experience working on Cloud, Azure is a plus
Experience working setting up data lakes using entire Big Data and DWH ecosystem
Experience on data workflows and ETL, Apache Airflow is a plus
Comfortable with Unix OS type systems, Bash and Linux