IT Data Infrastructure Architect - VP

We are seeking a Data Infrastructure Architect with 7+ years of experience in designing and governing enterprise-scale data platforms within regulated environments. The role ensures data is accessible, trusted, secure, and optimized for analytics, machine learning, and AI-driven solutions. Ideal candidates will either already have transitioned to or be ready to transition from a hands-on delivery role to strategic influence, shaping data architecture standards and guiding technology decisions across the organization.

The Architecture & Modernization (A+M) team drives the Firm's multi-year technology modernization roadmap, focusing on architecture frameworks, product delivery, and developer enablement. We aim to modernize technologies and practices to deliver scalable, secure, and future-ready  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.

This is a Principal Infrastructure Production Management & Reliability Engineering position at VP level, which is part of the job family responsible for maintaining the stability and reliability of the organization's infrastructure systems, ensuring optimal performance and availability to support business operations

Since 1935, Morgan Stanley is known as a global leader in financial services, continuously evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you’ll do in the role:

Architecture & Design Develop and execute data infrastructure architecture strategies, roadmaps, and blueprints for enterprise IT initiatives.

Divisional Data Officer driving data architecture & governance, working in close collaboration with Firmwide Data Office and other Divisional Data Officers Collaborate with SME teams building and scaling data platforms (data lakes, warehouses) across on-prem, cloud, and hybrid environments.

Implement semantic layers for consistent, governed data views, enabling self-service analytics and explainable AIOversee data quality processes and enforce standards to minimize data duplication and ensure trusted data.

Recommend and implement cloud-native solutions for scalability, resilience, and business continuity.

Drive modernization of infrastructure systems through cloud adoption, automation, and software-defined technologies.

Governance & Compliance Conduct architecture reviews ensuring alignment with regulatory, security, and enterprise standards.

Maintain reference architectures, policies, and best practices for data infrastructure.

Ensure robust data protection, encryption, and zero-trust security principles.

Collaboration & Stakeholder Engagement Promote architectural best practices, resolve design challenges, and influence technical decisions.

Partner with enterprise architects, engineering teams, and SREs to align solutions with IT strategy.

Provide technical leadership and guidance to engineering and operations teams. Required Technical Skills Data Platforms:
Experience in building & scaling data lakes, data warehouses, and associated data systems.

Databases: SQL, PostgreSQL;
MongoDB and cloud-based platforms (e.g. Snowflake, Databricks, Redshift).Semantic Layer:
Design and implementation for governed reusable data views.

Data Governance Tools:
Collibra, Alation, Talend or equivalent.

Expertise in ETL tools and processes (e.g., Apache NiFi, Talend, Informatica)
Security:
Data Privacy, Data Protection & Encryption frameworks & governance

Enterprise Architecture Frameworks: TOGAF, Zachman; familiarity with ITIL processes.

Additional Helpful

Skills:

Cloud Platform Awareness:
One or more of AWS, Azure and /or GCPAutomation & IaC:
Terraform, Ansible, Puppet; CI/CD pipelines (Jenkins, Git Hub Actions).Observability & Monitoring:
Experience integrating observability and monitoring solutions (Splunk, Prometheus, Loki, Grafana).AI & Advanced Analytics Exposure:

LLM Integration:
Retrieval-augmented generation, fine-tuning, prompt engineering.

Generative AI:
Building and deploying generative AI solutions for data-driven insights and automation.

Agen…