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

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

Analytics & Reporting

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

Integrations & Monitoring

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

Development & Collaboration

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  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

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

Experience

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  3–5 years’ experience in Data Engineering or related roles.
• 
  Hands-on experience with Databricks.
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  Experience with MLflow and machine learning deployment processes.
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  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
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  Ability to work in collaborative environments
• 
  Self-driven and proactive mindset