Design, develop, and maintain scalable ETL/data integration pipelines to ingest and transform data from multiple sources.
Build and optimize data processing solutions using Python, SQL, and Big Data technologies such as Hadoop and Spark.
Ensure data quality, integrity, and consistency across the entire ETL lifecycle.
Develop data integration processes to consolidate structured and unstructured data from diverse banking systems.
Optimize ETL jobs and database queries for performance, scalability, and reliability.
Implement and maintain data models aligned with business and reporting requirements.
Collaborate with business stakeholders, data analysts, and application teams to understand data requirements and deliver efficient data solutions.
Troubleshoot production issues and provide ongoing support for data pipelines and integration processes.
Follow data governance, security, and compliance standards within the banking environment.

Requirements

4+ years of experience as a Data Engineer, preferably in the banking or financial services industry.
Strong hands-on experience with Data Integration and ETL development.
Proficiency in Python and SQL for data processing and automation.
Experience with Big Data technologies such as Hadoop, Spark, or similar frameworks.
Good understanding of data modelling principles and best practices.
Experience working with large-scale datasets and distributed data processing.
Strong analytical, problem-solving, and debugging skills.
Excellent communication and stakeholder management skills.

Banking Data Engineer (ETL/Bigdata)