Data Architect

Position: Data/Snowflake Architect

Location: NYC, NY (3 days onsite is must)

Duration: 12 Months 

We are seeking an experienced Data Architect to join our Assurance & Legal Technology department overseeing Snowflake/Postgres distributed database platform deployments. This role involves comprehensive data analysis, data schematic design, performance tuning, and leveraging modern technologies to source, sanitize, and curate data into a gold data source for advanced analytics and reporting. You will play a key role in driving modernization, automation, and performance optimization across our Cloud/On-Prem data-platform ecosystem. 

Responsibilities 

• Design, implement, and manage large-scale distributed database systems using Snowflake and Postgres.  

• Lead the architecture and deployment of database clusters across hybrid cloud environments (AWS, Azure).  

• Develop and maintain automation frameworks for provisioning, monitoring, and scaling database infrastructure.  

• Collaborate with data engineering, platform, and application teams to ensure high availability, security, and performance.  

• Drive adoption of best practices for schema design, query optimization, and data lifecycle management.  

• Implement robust backup, disaster recovery, and compliance strategies.  

• Mentor junior engineers and contribute to cross-training initiatives across database technologies. 

• Govern how both structured and unstructured data is ingested, processed, and engineered for context. 

• Ensure high availability, disaster recovery, and performance tuning across multi-region deployments. 

• Collaborate with senior product, engineering, and audit leadership to influence business strategy and ensure solutions are built on a sound architectural foundation. 

• Architect and oversee the design and development of data and context engineering capabilities using Snowflake and PostgreSQL. 

• Make critical design decisions that enable seamless integration into Data Analytics, Business Intelligence Reporting, and AI agent workflow capabilities. 

• Establish and enforce data governance, quality standards, and validation frameworks. 

• Guarantee the integrity, security, and reliability of all data solutions. 

• Drive modernization initiatives including cloud-native database adoption, serverless architecture, and AI/ML integration.  

• Evaluate and implement emerging technologies to improve platform efficiency and developer experience.  

• Drive the future of audit technology by leading architectural innovation. 

• Champion the evaluation and adoption of next-generation data platforms, knowledge graphs, and advanced data fabrics. 

• Act with integrity, professionalism, and personal responsibility to uphold Morgan Stanley’s respectful and courteous work environment. 

Qualifications 

• Minimum 10 years of recent experience in data architecture, data engineering, or a similar field. 

• Hands-on expertise with PostgreSQL and Snowflake administration and performance tuning.  

• Strong scripting and automation skills (Python, Ansible, etc.).  

• Familiar with Cloud Based Architectures /Data-Warehousing Platforms, prior experience / knowledge in Snowflake Design Patterns and migration workflow would be a plus. 

• Prior exposure building ML and generative AI solutions using Snowflake Cortex Analyst, Search, AI SQL, Snowflake ML, and Snowpark Container Services. 

• Proven record of architecting and delivering large-scale data warehouse modeling and reporting solutions for enterprise systems. 

• A bachelor’s degree from an accredited college or university is required. A master''s degree in computer science, engineering, or a related technical discipline is preferred. 

• Expertise in architecting cloud-native data ecosystems using platforms such as Snowflake, Databricks, Talend, SPARK, Power BI, and Microsoft Fabric. 

• Deep knowledge of designing solutions that incorporate Retrieval-Augmented Generation (RAG) and vector search (e.g., Azure AI Search). 

• In-depth understanding of architectural patterns for processing and modeling both structured transactional data and unstructured information. 

• Experience with transactional processing and AI/ML applications. 

• Demonstrated experience defining architectural principles, data governance policies, and technology standards within a large-scale, regulated, and Agile Software Development Life Cycle (SDLC) environment. 

• Exceptional problem-solving, communication, collaboration and leadership skills. 

• Ability to articulate strategic technical vision, influence executive-level stakeholders, and guide solutions for complex business problems.