Data Engineer (MLOps / Analytics Focus)

Our client is seeking a capable intermediate-level Data Engineer with strong MLOps and analytics experience to support the design, optimisation, governance, and monitoring of enterprise data and machine learning pipelines.

The successful candidate will play a critical role in ensuring scalable, sustainable, and efficient data processes while supporting analytics, ML model deployment, integrations, and reporting initiatives within a Databricks ecosystem.

This opportunity offers strong long-term potential, as contractors are typically retained for multi-year engagements.

Requirements

Key Responsibilities

Data Engineering & Pipeline Management

• Design, optimise, and maintain scalable data pipelines within Databricks.
  
• Ensure pipelines are efficient, sustainable, easy to debug, and user-friendly.
  
• Implement and maintain Delta Tables and Databricks notebooks.
  
• Perform data validation and basic data quality checks.
  
• Monitor and improve process governance and operational efficiency.
  

MLOps & Machine Learning

• Train, deploy, and monitor machine learning models using MLflow.
  
• Analyse model performance and business impact.
  
• Support model lifecycle management and deployment best practices.
  

Analytics & Reporting

• Develop Power BI dashboards and business insight reporting.
  
• Support data-driven decision-making through analytics solutions.
  

Integrations & Monitoring

• Monitor API data integrations and data sends.
  
• Troubleshoot integration failures and ensure data consistency.
  

Development & Collaboration

• Manage Git-based workflows including:
  
  • Pull requests
    
  • Branch syncing
    
  • Merge conflict resolution
    
• Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders.
  

Minimum Requirements

Qualifications

• Degree or Diploma in:
  
  • Computer Science
    
  • Data Engineering
    
  • Information Systems
    
  • Mathematics
    
  • Statistics
    
  • or related field
    

Experience

• 3–5 years’ experience in Data Engineering or related roles.
  
• Hands-on experience with Databricks.
  
• Experience with MLflow and machine learning deployment processes.
  
• Experience with Power BI dashboard development.
  
• Strong experience with Git version control workflows.
  
• Exposure to API integrations and monitoring.
  

Technical Skills

• Databricks
  
• Delta Tables
  
• Databricks Notebooks
  
• MLflow
  
• Python
  
• SQL
  
• Power BI
  
• Git / Azure DevOps
  
• API Monitoring & Integration
  
• Data Pipeline Optimisation
  
• Data Quality & Governance
  

Advantageous Skills

• Azure Data Services
  
• CI/CD for ML Pipelines
  
• Spark / PySpark
  
• Cloud-based data platforms
  
• MLOps best practices
  

• Strong analytical and problem-solving abilities
  
• Attention to detail
  
• Strong communication skills
  
• Ability to work in collaborative environments
  
• Self-driven and proactive mindset