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 / Py.

Spark Cloud-based data platforms MLOps best practices Soft Skills Strong analytical and problem-solving abilities Attention to detail Strong communication skills Ability to work in collaborative environments Self-driven and proactive mindset