Job Description
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
