GenAI & Data Analytics Engineer

Morgan-Stanley; Town of Islip (NY); On-site; USD 81,000 - 135,000; Full time

Job summary

Morgan Stanley is seeking an Associate-level Data & Analytics Engineer to design and deploy GenAI and data analytics solutions, including DaiZy, across enterprise AI platforms. You will build scalable Python backends, REST APIs, and microservices, collaborating with cross-functional teams and clients to deliver data-driven insights.

The role emphasizes hands-on Snowflake experience, LLM integrations, and robust testing practices in a consulting environment.

Qualifications

• 3–5 years of software development or consulting experience.
• Strong Python proficiency with FastAPI, Flask, or Django.
• Experience with Git workflows, CI/CD, and automated testing.
• Hands-on Snowflake experience (data migration, querying, app integration).
• Proficiency integrating LLMs via hosted model APIs or enterprise AI platforms.

Responsibilities

• Design, build, and maintain backend services, REST APIs, and integrations using Python.
• Build distributed services in a microservices architecture with token-based auth and role-based access controls.
• Integrate internal apps with third-party systems, external APIs, and Snowflake.
• Develop LLM-powered features: model APIs, prompt workflows, structured outputs, and retrieval-based (RAG) apps.
• Write clean, modular, secure code with unit, integration, and automated tests.
• Collaborate with architects, engineers, data teams, product owners, and clients; translate requirements into implementation plans and clear technical documentation.

Skills

Python

REST APIs

Snowflake

Git workflows

CI/CD

LLMs

Microservices

Tools

FastAPI

Flask

Django

Node.js/TypeScript

Job description

Morgan Stanley is seeking an Associate-level Data & Analytics Engineer to design and deploy GenAI and data analytics solutions, including DaiZy, across enterprise AI platforms. You will build scalable Python backends, REST APIs, and microservices, collaborating with cross-functional teams and clients to deliver data-driven insights.

The role emphasizes hands-on Snowflake experience, LLM integrations, and robust testing practices in a consulting environment.