Key Responsibilities
- Analyze complex datasets to understand structures, relationships, and data quality issues
- Define source-to-target mappings and detailed transformation logic for data pipelines
- Develop and maintain conceptual, logical, and physical data models aligned with business requirements
- Translate business requirements into clear pseudo-logic and implementation-ready specifications
- Perform data reconciliation and variance analysis between legacy and new platforms
- Identify and resolve data issues through structured root cause analysis and debugging
Requirements
- Strong experience in data modeling (conceptual, logical, physical) and data analysis
- Advanced SQL skills for profiling, validation, and complex queries
- Experience defining transformation logic and pseudo-code for data pipelines
- Good understanding of data warehousing and ETL/ELT processes
- Knowledge of PySpark and SparkSQL for scalable data processing