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Commercial Bank of Qatar

Requisition ID 7100

Doha, Qatar

 

QUALIFICATION & EXPERIENCE:

  • Python (Advanced level)
  • Airflow or Apache NiFi
  • K8s (OpenShift), Docker
  • RDBMS: MS SQL Server, PostgreSQL, Oracle
  • ETL (at least one of): SSIS, Informatica PowerCenter, IBM Datastage, Pentaho
  • SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries)
  • Git (GitHub/GitLab)
  • Kafka is a plus
  • Object Storage (S3, GCS, ABS) is a plus
  • Spark is a plus
  • Experience with dbt is a plus
  • Familiar with Data Warehousing (Star/Snowflake schemas) And Data Lake
  • Agile methodologies (Kanban, Scrum)
  • Strong problem-solving skills

 

ROLES AND RESPONSIBILITIES:

Data Engineer is one of the key roles in a major initiative of Enterprise Data Fabric development which is a part of CBQ’s Data Strategy and overall Digital Transformation. As a part of Data Governance and Engineering department Data Engineer will be implementing various data management systems such as Data Lake, RDM, Data Streaming, Metadata management which will be a part of the Enterprise Data Fabric. Data Engineer will also work closely with number of IT, PMO, Data and operations teams to understand their needs and ensure that the Enterprise Data Fabric contributes to overall data quality, availability, democratization and culture.

  • Participate in Data Management systems implementation projects: Data Lakehouse, Data Streaming, Metadata management, Reference Data Management
  • Develop data pipelines to bring new data to Enterprise Data Fabric
  • Ensure data pipelines are efficient, scalable, and maintainable
  • Comply with data engineering and development best practices (CI/CD, Code Management, Testing, Knowledge Management, Documentation etc.)
  • Ensure that all Data Policies are met within Enterprise Data Fabric.
  • Ensure that implemented systems correspond with target Data Architecture
  • Support Data teams (DQ, Data Governance, BI, Data Science) in achieving their goals
  • Maintain agile delivery process based on one of frameworks: Kanban, Scrum
  • Ensure that SLAs with data consumers and data sources are maintained
  • Implement all necessary monitoring, alerting
  • Facilitating collaboration between IT and wider Data team and supporting data science, BI and Data Excellence teams to ensure seamless integration and effective communication.
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