Banking Data Engineer (ETL/Bigdata)

• 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.