الوصف الوظيفي
We are looking for a skilled Data Engineer to join the Data Engineering Chapter supporting the Group Operations team at ENBD.
The ideal candidate will be responsible for building scalable data pipelines, performing data analysis, and delivering high-quality data solutions aligned with enterprise data models.
Key Responsibilities Collaborate with the Group Operations Team to understand business and data requirements on a daily basis Perform Impact Assessment for new and existing data changes Conduct Technical Data Mapping and Data Profiling activities Design, develop, and maintain ETL pipelines for data extraction, transformation, and loading Build data solutions to feed into AECB application as per prescribed data models Develop and optimize data pipelines using PySpark on modern data platforms Ensure data quality, consistency, and integrity across systems Perform unit testing, debugging, and deployment of data solutions Leverage modern tools and AI technologies (e.
g., Claude) to enhance development efficiency and reduce operational errors Work closely with cross-functional teams including business analysts, architects, and QA Required Skills & Qualifications Strong hands-on experience with PySpark and distributed data processing Experience in Informatica BDM Development (Big Data Management) Solid understanding of ETL/ELT concepts and data pipeline architecture Expertise in data mapping, data profiling, and impact analysis Experience working with large-scale data systems and cloud/data platforms Strong SQL skills and understanding of data warehousing concepts Familiarity with banking/financial domain (preferred but not mandatory) Knowledge of AI-assisted development tools (e.
g., Claude) is a plus Good problem-solving and analytical skills Nice to Have Experience with AECB data/reporting systems Exposure to big data ecosystems (Hadoop/Spark clusters) Understanding of data governance and compliance standards
لقد تمت ترجمة هذا الإعلان الوظيفي بواسطة الذكاء الاصطناعي وقد يحتوي على بعض الاختلافات أو الأخطاء البسيطة.
