Principal Data Engineer - Parametric

About Morgan Stanley

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

Parametric is part of Morgan Stanley Investment Management, the asset management division of Morgan Stanley. We partner with advisors, institutions, and consultants to build portfolios focused on what s important to them and their clients. A leader in custom solutions for more than 30 years, we help investors access efficient market exposures, solve implementation challenges, and design multi-asset portfolios that respond to their evolving needs.

We also offer systematic alpha and alternative strategies to complement clients  core holdings.

This role is part of Parametric s hybrid working model, which includes working in the office 3 days a week and choosing to work remotely or in the office the remaining days of the week.

The Data Engineering team ensures that data pipelines are scalable, repeatable, secure, and can serve multiple users. We help facilitate getting data from a variety of different sources, getting it in the right formats, assuring that it adheres to data quality standards, and assuring that downstream users can get that data quickly.

The Principal Data Engineer will serve as a technical lead on new and ongoing development projects, designing, developing, and maintaining services to support various business needs. In this role you will be responsible for guiding and mentoring a team of data engineers helping them to achieve their highest potential while collectively advancing the goals and projects of the engineering team.

The Principal Data Engineer is responsible for the design, structure, and maintenance of data projects ensuring the accuracy and accessibility of data relevant to an organization or a project. In this role the Principal Data Engineer needs to be able to take business requirements and design and architecture end to end that fully supports the business needs. The Princip

LData engineer needs to be an expert in writing and optimizing SQL queries. They need to be able to document ideas and proposals which will include creating ER diagrams and architectural documents that document the transformation of data throughout its lifecycle.

Asuccessful Principal Data Engineer must possess superior analytical skills and be detail-oriented. This role requires the ability to communicate effectively as part of a larger team within the information technology department. Additionally, you will need to explain complex technical concepts to non-technical staff. Since development of data models and logical workflows is common, a Principal Data Engineer must also exhibit advanced visualization skills, as well as creative problem-solving.

• AI & Automation Integration:
  Design and implement data pipelines optimized for machine learning, generative AI, and real-time analytics.
• Modern Data Architecture:
  Build scalable, cloud-native architectures leveraging Data Mesh, Lakehouse, and streaming technologies.
• Data Governance & Ethics:
  Enforce responsible AI practices, data privacy, and compliance with evolving regulations.
• Performance Optimization:
  Apply advanced techniques for query optimization, distributed computing, and cost-efficient cloud operations.
• Legacy Modernization:
  Reimagine legacy systems using containerization, serverless computing, and AI-driven orchestration.
• Collaboration & Leadership:
  Partner with business and technical stakeholders to align solutions with strategic objectives.
• Documentation & Standards:
  Maintain technical specifications, runbooks, and architectural diagrams.
• Security & Resilience:
  Implement zero-trust security models, disaster recovery, and automated failover strategies.
• Continuous Innovation:
  Evaluate emerging technologies (e.g., AI agents, quantum-ready data frameworks) and integrate where applicable.
• Evaluates and provides feedback on future technologies and new releases/upgrades.
• Contributes to the establishment of business continuity & disaster recovery requirements, methods and procedures for data systems and databases.

• Bachelor s in Computer Science, Mathematics, or Engineering or equivalent work experience
• 10+ years of data engineering, data science, or software engineering experience
• 3+ years of experience with Snowflake
• 4+ years of experience in AWS (or similar cloud technologies) cloud stack including S3, IAM, Athena, SNS, SQS, and EMR.
• 6+ years of SQL Server and experience with tools such as SSIS and SSRS
• Demonstrated success providing both expert technical guidance and team leadership
• Capability of architecting highly scalable distributed systems, using different open-source tools
• Highly skilled in…