WE ARE HIRING!

A leading organisation in the healthcare and manufacturing sector is looking to appoint a dynamic, passionate, confident, proactive, and meticulous Data Analyst to join a goal -oriented team.

The successful candidate will play a key role in transforming data into actionable business insights, developing scalable data solutions, and supporting data -driven decision -making across the organisation.

QUALIFICATIONS NEEDED

Required:

• BSc / BCom in Computer Science, Statistics, Data Science, Mathematics, or related field
  

Preferred:

• Honours or postgraduate degree in Data Science or Applied Mathematics
  
• Experience in pharmaceutical, life sciences, or regulated manufacturing environments (GxP advantage)
  
• Prior pharmaceutical or regulated manufacturing experience is advantageous
  

EXPERIENCE REQUIRED

• Proven delivery of end -to -end data projects from raw data through to business output
  
• Strong proficiency in Python for data manipulation, modelling, and automation
  
• Hands -on experience with Microsoft Fabric (Lakehouses, Pipelines, Notebooks, Semantic Models)
  
• Power BI dashboard and report development including DAX and data modelling
  
• SQL and relational/non -relational database expertise
  
• Data engineering fundamentals including ETL/ELT processes
  
• Experience with data visualisation, reporting, and storytelling
  
• Knowledge of cloud -based data architecture and platforms
  

Preferred Experience:

• Exposure to Azure Data Services (Synapse, Data Factory, ADLS)
  
• Experience with MLOps and model deployment
  
• Familiarity with GMP/GxP data integrity requirements
  

Requirements

KEY DUTIES & RESPONSIBILITIES

Core Technical Delivery

Data Engineering & Pipeline Management

• Design, build, and maintain scalable ETL/ELT pipelines within Microsoft Fabric
  
• Ingest, cleanse, and transform structured and unstructured datasets
  
• Manage Lakehouse architecture, data flows, and Fabric workspaces
  
• Monitor pipeline performance and implement data quality controls
  
• Ensure data lineage, cataloguing, and governance compliance
  

Analytics, Modelling & Insights

• Develop statistical and machine learning models to solve business challenges
  
• Conduct exploratory data analysis and communicate findings to non -technical stakeholders
  
• Build and maintain Power BI dashboards and semantic models
  
• Translate business requirements into data hypotheses and actionable insights
  
• Conduct forecasting and trend analysis where required
  

Quality & Data Governance

• Ensure all data processes comply with organisational governance and quality standards
  
• Maintain audit -ready documentation of data models, pipelines, and outputs
  
• Support the development of a strong data quality culture across the organisation
  
• Identify and remediate data integrity risks proactively
  

Collaboration & Stakeholder Engagement

• Collaborate with business units and leadership teams to prioritise data requirements
  
• Present findings, reports, and dashboards to management and executive stakeholders
  
• Mentor junior analysts where applicable
  
• Work closely with IT teams regarding infrastructure and data security requirements
  

TECHNICAL COMPETENCIES

• Ability to communicate technical concepts to non -technical audiences
  
• Statistical modelling and machine learning using libraries such as scikit -learn and statsmodels
  
• Power BI expertise including DAX, Power Query, and Semantic Model design
  
• Data visualisation best practices
  
• Experience with Notebooks, VS Code, and Fabric Notebooks
  
• Microsoft Fabric (Pipelines, Dataflows, Lakehouses, Notebooks)
  
• Python (pandas, PySpark, NumPy)
  
• SQL / T -SQL / KQL
  
• Azure Data Lake Storage and cloud data infrastructure
  
• ETL design patterns and orchestration
  
• Data governance frameworks and metadata management
  
• Enterprise data platforms including Microsoft Fabric, Azure, and Power BI
  
• Data engineering and integration methodologies
  
• Data storytelling and business insight generation
  
• Cloud -based data architecture principles