RQ00527 - DataOps/Cloud Data Engineer - Senior

Key Responsibilities

• Design, build, and maintain end to end data pipelines using Microsoft Fabric, including Lakehouse, Data Factory, Dataflows, and notebooks.
  
• Develop and optimize SQL based transformations, data models, and curated datasets for enterprise reporting and analytics.
  
• Build and maintain Python based data engineering logic for ingestion, transformation, validation, and automation.
  
• Implement and operate data quality controls, including validation rules, reconciliation checks, and exception handling.
  
• Monitor data pipelines, investigate failures or data quality issues, and implement fixes with minimal escalation.
  
• Integrate data from multiple enterprise systems, including CRM, ticketing systems, telephony platforms, and operational databases.
  
• Maintain technical documentation, data lineage, and operational runbooks for owned pipelines and datasets.
  
• Work closely with the Manager and Data Analysts to ensure data assets meet defined standards and reporting requirements.
  

• Minimum 7 years of hands-on experience in data engineering or analytics engineering roles.
  
• Strong, practical experience with Microsoft Fabric in a production environment.
  
• Advanced proficiency in SQL, including complex transformations and performance optimization.
  
• Strong hands-on experience using Python for data engineering and automation.
  
• Demonstrated experience implementing data quality controls and operating reliable data pipelines.
  
• Experience working in cloud-based data platforms and modern data architectures.
  
• Ability to deliver independently while aligning to technical direction and priorities set by management.
  
• Microsoft certifications preferred such as Microsoft certified Fabric analytics engineer, Azure data engineer or Microsoft data platform certification.
  
  

• Degree in Computer Science, Engineering, or a related technical field.
  
• Experience supporting Power BI semantic models and enterprise reporting environments.
  
• Familiarity with Git, version control, and basic CI/CD practices for data engineering.
  
• Experience working in regulated, operational, or public sector environments.
  
  

Requirements

Must Haves:

• Minimum 7 years Demonstrated hands-on experience building and maintaining end-to-end data pipelines in production
  
• Minimum 7 years Strong expertise in SQL and Python from an engineering standpoint
  
• Minimum 7 years Experience implementing data quality controls, monitoring, and troubleshooting with minimal escalation
  
• Minimum 7 years Practical experience with modern data platforms (e.g., Microsoft Fabric or similar enterprise environments)