Data Engineer

Architecture & Migration: Design and develop end-to-end data migration pipelines using AWS (S3, Glue, Redshift, Lambda) and Amazon-internal data tools to transition data from legacy CRM systems to Salesforce. Pipeline Engineering: Build scalable, maintainable pipelines for both structured and unstructured data, ensuring high performance for batch processing Data Modeling: Design and implement optimized data models within Redshift and Salesforce to support analytics and downstream AI-native applications. Cross-Functional Collaboration: Partner with Software Developers, BIEs, and Product Managers to gather functional requirements and translate them into technical specifications. Operational Excellence: Implement automated monitoring, data quality checks, and error-handling frameworks to ensure 100% data accuracy during the migration lifecycle. Ownership, Customer obsession & Deliver results 3+ years of professional experience in Data Engineering, specifically focusing on large-scale migrations. Proven ability to write complex SQL, highly optimized queries and perform deep-dive data analysis. Hands-on experience with AWS Redshift, S3, Glue, and Lambda . Programming: Proficiency in Python (preferred) or Java/Scala for data manipulation and automation. ETL/ELT Proficiency: Experience with modern ETL frameworks and legacy tools (e.g., Informatica, SSIS) to facilitate reverse engineering of undocumented legacy systems. Data Modeling: Strong understanding of dimensional modeling, Star/Snowflake schemas, and data warehousing best practices. Direct experience with Salesforce Data Loader, Bulk API , and architecting data flows between AWS and Salesforce. Experience with QuickSight or Tableau to build validation dashboards that track migration progress and data parity Experience in Demonstrated success in reverse-engineering legacy analytical applications where documentation is sparse. Experience in building AI agents to perform code refactoring and code migrations