IT Data Infrastructure Architect - VP
Listed on 2025-12-20
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IT/Tech
Data Engineer, Cloud Computing, Data Security
IT Data Infrastructure Architect - Vice President
Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management, and wealth management services. Our success depends on the talent and passion of our people. We offer a superior foundation for building a professional career where you can learn, achieve, and grow.
Technology is the key differentiator that enables us to manage global businesses and serve clients on a resilient, secure, and innovative platform. Our award‑winning technology platforms drive business excellence and innovation across complex financial markets.
Technology Architecture & ModernizationThe 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 platforms.
Role OverviewWe 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.
Key Responsibilities- Architecture & Design:
Develop and execute data infrastructure architecture strategies, roadmaps, and blueprints for enterprise IT initiatives. - Divisional Data Officer:
Drive 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 AI.
- Oversee 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.
- 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.
- 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.
- Data Platforms:
Experience in building & scaling data lakes, data warehouses, and associated data systems. - Databases: SQL, Postgre
SQL;
Mongo
DB 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.
- Cloud Platform Awareness:
One or more of AWS, Azure and /or GCP. - Automation & IaC:
Terraform, Ansible, Puppet; CI/CD pipelines (Jenkins, Git Hub Actions). - Observability & Monitoring:
Experience integrating observability and monitoring solutions (Splunk, Prometheus, Loki, Grafana).
- LLM Integration:
Retrieval‑augmented generation, fine‑tuning, prompt engineering. - Generative AI:
Building and deploying generative AI solutions for data‑driven insights and automation. - Agentic AI:
Designing…
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