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.
RequirementsKey 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.
- Train, deploy, and monitor machine learning models using MLflow.
- Analyse model performance and business impact.
- Support model lifecycle management and deployment best practices.
- Develop Power BI dashboards and business insight reporting.
- Support data-driven decision-making through analytics solutions.
- Monitor API data integrations and data sends.
- Troubleshoot integration failures and ensure data consistency.
- Manage Git-based workflows including:
- Pull requests
- Branch syncing
- Merge conflict resolution
- Pull requests
- Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders.
Qualifications
- Degree or Diploma in:
- Computer Science
- Data Engineering
- Information Systems
- Mathematics
- Statistics
- or related field
- Computer Science
- 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.
- Databricks
- Delta Tables
- Databricks Notebooks
- MLflow
- Python
- SQL
- Power BI
- Git / Azure DevOps
- API Monitoring & Integration
- Data Pipeline Optimisation
- Data Quality & Governance
- 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
- Ability to work in collaborative environments
- Self-driven and proactive mindset
Skills Required
- Degree or diploma in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, or a related field
- 3-5 years of experience in data engineering or related roles
- Hands-on experience with Databricks
- Experience with MLflow and machine learning deployment processes
- Experience developing Power BI dashboards
- Strong experience with Git version control workflows
- Exposure to API integrations and monitoring
- Python and SQL experience
- Experience with Delta Tables and Databricks Notebooks
- Experience with Azure Data Services
- Experience with CI/CD for machine learning pipelines
- Experience with Spark or PySpark
- Experience with cloud-based data platforms
- Knowledge of MLOps best practices
What We Do
BluePearl is a U.S. network of specialty and emergency veterinary hospitals providing advanced medical care for pets, including innovative procedures, diagnostics, surgery, and other specialized services in communities across the United States. Its mission is to deliver exceptional care while advancing veterinary medicine and honoring the human-animal bond. Founded in 1996, BluePearl operates more than 100 hospitals and is part of Mars Veterinary Health.
