- Develop and execute complex SQL queries for data extraction, transformation, and validation across large-scale databases.

- Perform data profiling, data cleansing, and data quality checks to ensure accuracy and consistency.

- Work on data migration projects involving legacy banking systems analyzing, mapping, and validating migrated data.

- Utilize Informatica for ETL and data remediation activities, including troubleshooting and exception handling.

- Design and build interactive Power BI dashboards and reports to visualize operational and financial data.

- Collaborate with data engineering and business teams to define data requirements, mappings, and transformation logic.

- Support ETL job validation, data reconciliation, and data lineage documentation.

- Conduct root cause analysis for data discrepancies and implement corrective data logic.

- Optimize data workflows for performance and scalability.

- Ensure adherence to data governance, compliance, and security policies.

Technical Skills Required :

Database & Querying :

- Strong SQL development skills (Oracle / MS SQL / PostgreSQL).

- Expertise in joins, subqueries, analytical functions, performance tuning, and query optimization.

ETL & Data Integration :

- Hands-on experience with Informatica PowerCenter or similar ETL tools for data remediation and migration.

- Understanding of data mapping, data lineage, and transformation logic.

Visualization & Reporting :

- Experience developing dashboards and reports in Power BI (DAX, Power Query, data modeling).

- Ability to design self-service BI solutions and automate report refreshes.

Data Migration & Legacy Systems :

- Working knowledge of core banking data structures, legacy databases, and migration validation strategies.

- Familiarity with data reconciliation techniques and exception reporting.

Data Quality & Validation :

- Strong understanding of data validation rules, error handling, and remediation frameworks.

General Tools & Environment :

- Familiarity with Excel (advanced), Python/R (for data analysis), and Azure Data Lake / Cloud data environments (good to have).