Banking Data Engineer (ETL/Bigdata)
Summary
Design and maintain scalable ETL pipelines for banking data using Python, SQL, Hadoop, and Spark to ensure reliable, high-quality data integration and processing.
- 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