Data Engineer (MLOps / Analytics Focus)

About the role

Our client is looking for an intermediate‑level Data Engineer with a strong focus on MLOps and analytics. The role will involve designing, optimizing and governing enterprise‑scale data and machine‑learning pipelines within a Databricks environment, ensuring they are scalable, maintainable and business‑ready.

Key responsibilities

• Design, optimise and maintain scalable data pipelines in Databricks, including Delta Tables and notebooks.
• Implement data validation, quality checks and monitor pipeline governance.
• Train, deploy and monitor ML models using MLflow, analysing performance and business impact.
• Develop Power BI dashboards and reporting solutions to support data‑driven decisions.
• Monitor API integrations, troubleshoot failures and ensure data consistency.
• Manage Git‑based workflows: pull requests, branch syncing, merge conflict resolution.
• Collaborate with data scientists, analysts and business stakeholders across projects.

Required profile

• Bachelor’s degree or diploma in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics or a related field.
• 3–5 years of hands‑on experience in data engineering or a similar role.
• Proven experience with Databricks, MLflow and Power BI.
• Strong knowledge of Git version control and API integration monitoring.
• Self‑driven, proactive and able to work collaboratively.

Required skills

• Databricks (Delta Tables, Notebooks)
• MLflow
• Python
• SQL
• Power BI
• Git / Azure DevOps
• API integration and monitoring
• Data pipeline optimisation
• Data quality and governance
• Azure Data Services (advantageous)
• CI/CD for ML pipelines
• Spark / PySpark
• Cloud‑based data platforms
• MLOps best practices