Data AI Engineering Specialist (Hybrid), Director

Morgan Stanley • Montreal, QC

46d • Hybrid

About The Position

As a Data AI Engineering Specialist within the Architecture & Modernization team, you will be instrumental in building and maintaining the data infrastructure for our Data AI platforms. This role will involve hands-on development, data pipeline creation, and close collaboration with stakeholders across the organization. This role requires a self-starter with strong execution skills and the ability to work independently. You will be expected to not only execute on the current strategy but also contribute to its evolution. We value diversity of thought and are committed to building a team that reflects the diversity of our global community.
Our mission is to develop a firmwide Artificial Intelligence (AI) Development Platform that aligns with the firm's Technology principles and drives efficiency and consistency, controls, security and strong governance and promotes innovation, enabling teams to build applications that leverage AI capabilities and accelerate the adoption of AI across our businesses.
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.
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.
Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on…

Requirements

• 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.
• 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.

Nice To Haves

• Experience on Snowflake Cortex will be really appreciated.
• Familiarity with data visualization tools (e.g., Tableau, Power BI).
• Familiarity with data governance and security best practices (e.g., data access control, data masking).
• Experience with Agile methodologies.
• Familiarity with data catalog and metadata management tools (e.g., Collibra).
• Familiarity with CI/CD pipelines and DevOps practices.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees