About the role

Data AI Engineering Specialist building and maintaining data infrastructure for Data AI platforms at Morgan Stanley. Collaborating with stakeholders and handling data pipelines and governance standards.

Responsibilities

Develop and maintain data pipelines and ETL (Extract, Transform, Load) processes
Work with structured and unstructured data to ensure it is accessible and usable
Optimize data systems for performance and scalability
Implement data quality and data governance standards
Collaborate with stakeholders across technology and business units to understand their data needs and translate them into technical solutions and provide data-driven insights
Contribute to the documentation and knowledge sharing within the team, creating, and maintaining technical documentation and training materials
Participate in code reviews and contribute to the improvement of development processes
Contribute to the broader data architecture community through knowledge sharing, presentations

8 years+ of being a practitioner in data engineering or a related field
Proficiency in programming skills in Python
Experience with data processing frameworks like Apache Spark or Hadoop
Knowledge of database systems (SQL and NoSQL)
Experience working on Snowflake and Databricks
Experience on Snowflake Cortex will be really appreciated
Familiarity with cloud platforms (AWS, Azure) and their data services
Understanding of data modeling and data architecture principles
Experience with data warehousing concepts and technologies
Experience with message queues and streaming platforms (e.g., Kafka)
Experience with version control systems (e.g., Git)
Experience using Jupyter notebooks for data exploration, analysis, and visualization
Excellent communication and collaboration skills
Ability to work independently and as part of a geographically distributed team.