MLOps‑Driven Data Engineer | Databricks & Analytics

Blue Pearl HQ; Johannesburg; On-site; ZAR 720,000 - 900,000; Full time

Job summary

Blue Pearl HQ is seeking an intermediate Data Engineer with strong MLOps and analytics experience to design, optimise, govern, and monitor enterprise data and ML pipelines within a Databricks ecosystem.

You will support analytics, ML model deployment, integrations, and reporting initiatives, collaborating with data scientists, analysts, and business stakeholders on long-term engagements.

Qualifications

• Degree or Diploma in Computer Science, Data Engineering, Information Systems, Mathematics or Statistics
• 3–5 years’ experience in Data Engineering or related roles
• Hands-on experience with Databricks
• Experience with MLflow and ML deployment processes
• Experience with Power BI dashboard development
• Strong experience with Git version control workflows and API integrations

Responsibilities

• Design, optimise, and maintain scalable data pipelines within Databricks
• Ensure pipelines are efficient, maintainable, and easy to debug
• Implement and maintain Delta Tables and Databricks notebooks
• Perform data validation and basic data quality checks
• Monitor and improve governance and operational efficiency
• Train, deploy, and monitor ML models using MLflow
• Develop Power BI dashboards and analytics reporting
• Manage API data integrations and monitor data sends
• Collaborate with data scientists, analysts, and business stakeholders
• Collaborate using Git-based workflows (PRs, branches, merges)

Skills

Data Engineering

ML Ops

Python

SQL

Power BI

Education

Computer Science

Data Engineering

Information Systems

Mathematics

Statistics

Tools

Databricks

Delta Tables

Databricks Notebooks

MLflow

Git / Azure DevOps

API Monitoring

Job description

Blue Pearl HQ is seeking an intermediate Data Engineer with strong MLOps and analytics experience to design, optimise, govern, and monitor enterprise data and ML pipelines within a Databricks ecosystem.

You will support analytics, ML model deployment, integrations, and reporting initiatives, collaborating with data scientists, analysts, and business stakeholders on long-term engagements.