Job Description
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.

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

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

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

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

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

• 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.

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

• 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