Architect

منذ 3 ساعات

united arab emirates Virtusa دوام كامل

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