Data Engineer (MLOps / Analytics Focus)

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

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.

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.

Qualifications

Minimum Requirements

• 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 / PySpark
• 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

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