This role pays more than
64%
of similar roles. Most pay
$187,000–$273,087
— the shaded band above.
At the midpoint, this role pays about
$250k
versus about
$230k
for comparable roles.
Based on 240 similar postings.
Employer
About Morgan Stanley
Morgan Stanley is a global financial services firm providing investment banking, securities, wealth management, and investment management services to corporations, governments, institutions, and individuals. Industry: Investment Banking & Financial Services
Morgan Stanley currently has
39 open roles
on FindRole.
Listed pay typically runs
$150,000–$210,000
across 33 roles with salary data.
Executive Director & Head of Data Architecture - Morgan Stanley Investment Management serves as a pivotal leader within the investment management division, tasked with reimagining how data is organized, governed, and consumed across public and private markets. The role involves defining strategic blueprints for data architecture, establishing standards for ingestion, storage, and distribution while transitioning the firm toward reusable enterprise capabilities and defined data products. You will manage conceptual, logical, and physical models while overseeing multi-year roadmaps for analytical and AI-oriented platforms. Key technical competencies include experience with Snowflake, Databricks, Spark, Kafka, and cloud-native services, alongside knowledge of data mesh, vector databases, and RAG architecture. The role addresses the challenge of managing a complex, regulated investment landscape by ensuring data is treated as an enterprise asset to support advanced analytics, machine learning, and generative AI initiatives.
Define and maintain enterprise data architecture including conceptual, logical, and physical models and standards.
Develop multi-year roadmaps for operational, analytical, and AI-oriented data platforms.
Establish architectural patterns for data ingestion, integration, transformation, storage, and distribution.
Design architectures supporting cloud, lakehouse, warehouse, streaming, API, and event-driven use cases.
Define standards for data products, metadata, lineage, and master/reference data.
Partner with engineering teams to translate architecture into scalable and maintainable implementations.
Embed governance, quality, privacy, security, and regulatory requirements into all architectural designs.
Evaluate technology choices and trade-offs regarding scalability, performance, cost, and operational complexity.
What we're looking for
Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent professional experience.
Experience within financial services, asset management, banking, or another highly regulated industry (preferred).
Experience leading large-scale data migrations, platform transformations, and operating model conversions involving investment management platforms (preferred).
Deep understanding of investment management data domains including security master, holdings, positions, transactions, portfolios, benchmarks, performance, compliance, accounting, client reporting, risk, and product data (preferred).
Institutional experience and experience with Blackrock Aladdin and eFront product suite and data structure (preferred).
Knowledge of data mesh, data products, domain-oriented architecture, and federated governance concepts (preferred).
Familiarity with semantic layers, knowledge graphs, vector databases, RAG architecture, and AI-ready data patterns (preferred).
Experience with contemporary data platforms such as Snowflake, Databricks, Spark, Kafka, or cloud-native data services (preferred).
Snowflake
Databricks
Microsoft Fabric
Azure Data Services
Data Lakehouse
Delta Lake
Apache Iceberg
Kafka
Kubernetes
Apache Airflow
CI/CD
Infrastructure as Code
RAG
Vector Databases
LLMOps
MLOps
Data Mesh
Data Management
Data Architecture
Analytics
AI
Machine Learning
Master Data Management
Data Governance
Cloud Platforms
Business Intelligence
Data Modeling
Automation
GxP
NIST AI RMF
ISO 42001
Data Quality
Artificial Intelligence
Data Platforms
Cybersecurity
Infrastructure
Big Data
Machine Learning
Mobile Solutions
Technology Strategy
Business Development
Data Governance
Risk Management
Data Quality Management
Metadata Management
Data Stewardship
Cybersecurity
Site Reliability Engineering
AI Tools
Gap Assessment
Risk Mitigation
Reporting