ETL Developer

4+ years of experience in Data Engineering, preferably within the Banking/Financial Services domain, with experience working on large-scale enterprise data and production data pipelines.

Strong hands-on expertise in SQL, Python, and PySpark, including complex SQL queries, data transformations, aggregations, joins, window functions, and performance optimization.

Design, develop, and maintain scalable ETL/ELT pipelines for batch and incremental data processing across multiple banking source systems.

Strong experience with data warehousing and data modeling, including Fact/Dimension tables, Star Schema, SCD, historical data management, data marts, and analytical datasets.

Hands-on experience with SSIS for ETL/data integration and SSRS for operational/business reporting; exposure to Power BI or Tableau is preferred.

Experience with Databricks / Apache Spark for large-scale distributed data processing is highly desirable.

Experience with any major cloud platform (Azure, AWS, or GCP) and exposure to cloud-based data services, storage, and data processing platforms.

Understanding and hands-on experience with Change Data Capture (CDC) / data replication using tools such as Qlik Replicate or equivalent CDC technologies is preferred.

Strong focus on data quality, source-to-target reconciliation, data validation, error handling, monitoring, auditability, and production support, particularly for critical financial/banking data.

Good understanding of banking data and processes such as Customer, Account, Loan, Transaction, Payment, Risk, and Portfolio data, with strong problem-solving, stakeholder-management, and communication skills.