Confluent Kafka Data Engineer

Key Responsibilities  Design, develop, and maintain robust, scalable data pipelines using Azure Data Factory (ADF) and Databricks  Implement batch and real-time data ingestion frameworks leveraging Confluent Kafka  Build and optimize data models, transformations, and ETL/ELT pipelines using SQL and Spark  Develop and manage data storage solutions in Azure Data Lake Storage (ADLS)  Ensure high data quality, integrity, and governance standards  Support data integration across systems including core banking and CRM  Optimize performance of large-scale datasets  Collaborate with stakeholders to translate requirements into solutions  Ensure compliance with banking regulations and data privacy  Troubleshoot production issues Required Skills & Experience  Strong expertise in SQL (complex queries, tuning, modeling)  Hands-on experience with Databricks, Spark, PySpark  Experience with Confluent Kafka and event-driven architecture  Experience with Azure Data Factory (ADF)  Experience with Azure Data Lake Storage (ADLS)  Understanding of data warehousing concepts  Familiarity with CI/CD and DevOps for data platforms Domain Knowledge (Preferred)  Experience in banking or financial services Classification RAKBANK-Internal  Understanding of core banking systems  Knowledge of payments, lending, wealth management  Awareness of regulatory frameworks (KYC, AML) Soft Skills  Strong analytical and problem-solving skills  Excellent communication and stakeholder engagement  Ability to work in fast-paced environments  Strong ownership and collaboration mindset Qualifications  Bachelor’s or Master’s degree in relevant field  Azure/Data engineering certifications preferred Good to Have  Experience with Python or Scala  Exposure to data governance tools  Knowledge of APIs and microservices  Experience with BI tools like Power BI Key Success Indicators  Delivery of scalable pipelines  Improved data reliability  Reduced data latency  Alignment with business requirements