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
Bachelors or Masters 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