We are looking for two highly skilled Platform Data Engineers to join a cutting-edge Customer Save & Open API team.

This is not your typical data role — it’s a hybrid of Data Engineering and Azure Platform Engineering, where you will play a key role in building and enhancing a modern, scalable One Data Platform (ODP) aligned to enterprise-wide data strategy.

You’ll be working on high-impact data products, enabling advanced analytics, real-time insights, and next-generation customer experiences.

As a Platform Data Engineer, you will:

• Translate business requirements, architecture, and data models into scalable technical solutions
  
• Design and build metadata-driven data ingestion pipelines using Azure Data Factory and Databricks
  
• Develop and maintain the Enterprise Data Warehouse (Kimball methodology)
  
• Build data products using Databricks and Microsoft Fabric (Lakehouse, Warehouse, Pipelines, Semantic Models)
  
• Implement end-to-end data engineering lifecycle within the One Data Platform (ODP)
  
• Engineer real-time streaming solutions using Azure Event Hubs and Stream Analytics
  
• Drive DevOps excellence (CI/CD, automation, infrastructure-as-code)
  
• Ensure data quality, testing, and performance optimization across pipelines
  
• Design reusable Azure templates and deployment components
  
• Promote data governance, security, and best practices (Purview, Unity Catalog)
  
• Collaborate closely with business stakeholders and cross-functional teams
  
• Contribute to Group Data Engineering standards, frameworks, and best practices
  

Requirements

What You Bring

Core Experience

• 6+ years in Data Engineering / Platform Engineering
  
• Strong experience in Azure data ecosystem
  
• Hands-on expertise in Microsoft Fabric (Lakehouse, Pipelines, Semantic Models)
  
• Proven experience with Apache Spark for large-scale data processing
  
• Strong SQL skills (T-SQL) and data analysis capability
  

Technical Skills

• Azure Stack:
  • Azure Data Factory
    
  • Azure Event Hubs
    
  • Azure Synapse Analytics
    
  • ADLS Gen2
    
  • Azure Stream Analytics
    
• Databricks (including Unity Catalog)
  
• ETL/ELT pipeline development (batch & real-time)
  
• Data modelling: Kimball & Data Vault 2.0
  
• Programming: Python, SQL, C#
  
• Data tools: Pandas, Spark
  

DevOps & Automation

• Azure DevOps (Repos, Pipelines, CI/CD)
  
• Infrastructure as Code (Bicep, ARM, Azure CLI, PowerShell, Bash)
  
• Automated deployment pipelines
  

Security & Governance

• Azure Active Directory (Authentication & Authorization)
  
• Data governance tools (Microsoft Purview)
  
• Secure data design & compliance best practices
  

Nice to Have

• Experience in Banking / Financial Services
  
• Exposure to Open Banking technologies
  
• Knowledge of Data Mesh architectures
  

Qualifications & Certifications

• Bachelor’s degree in Computer Science or related field (or equivalent experience)
  
• Mandatory Azure Certification:
  • AZ-900 or DP-203 / DP-600 / DP-700
    
• Bonus:
  • Databricks Certifications (Fundamentals / Data Engineer Associate / Architect)