Develop conceptual, logical, and physical data models and design enterprise data architecture, including data warehouses, data lakes, and integration pipelines (ETL/ELT)
Job Summary
Develop conceptual, logical, and physical data models and design enterprise data architecture, including data warehouses, data lakes, and integration pipelines (ETL/ELT).
Select appropriate database technologies (SQL, NoSQL, Cloud platforms like Snowflake, AWS) and optimize data storage, retrieval, and processing.
Lead Integration solution between SaaS applications and Morgan Stanley.
Matching Summary
Develop conceptual, logical, and physical data models and design enterprise data architecture, including data warehouses, data lakes, and integration pipelines (ETL/ELT).
Skills & Requirements
Must-have
Develop enterprise data architecture
Design data warehouses and data lakes
Implement ETL/ELT integration pipelines
Select appropriate database technologies
Onboard firm standard ETL & AI tools
Design and support strategic reporting tools
Lead integration solution between SaaS applications
Nice-to-have
Continuous improvement across delivery
Collaborate with technical leads and analysts
Drive continuous improvement of integrations
Work with AI-driven insights or automation
Key Requirements
10+ years of experience as a hands-on developer
Experience with Python, Postgres, DB2, shell scripting
Experience with ETL technologies
Extensive experience with Python libraries like Pandas, PySpark
Proven experience leading large, complex engagements
Strong understanding of Agile frameworks
Exceptional stakeholder management and communication skills