Analytical Engineer
Role Purpose
The Analytical Engineer is responsible for transforming complex, raw data into trusted, analytics-ready datasets that support business intelligence, advanced analytics, and enterprise decision-making. This role bridges data engineering and analytics by combining data modelling, data transformation, and governance expertise to deliver high-quality data products.
Key Responsibilities
- Design and implement scalable data models using Data Vault and dimensional modelling methodologies.
- Develop and maintain ETL/ELT pipelines to ingest, cleanse, and transform data into trusted analytical datasets.
- Deliver analytics-ready data to support reporting, dashboards, AI, and advanced analytics initiatives.
- Ensure data quality, governance, lineage, and compliance with enterprise standards.
- Collaborate with business stakeholders, architects, data scientists, and delivery teams to translate business requirements into data solutions.
- Contribute to Agile delivery teams, solution design, and continuous improvement of data engineering practices.
Minimum Requirements
- Qualifications
- Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science, or a related field.
- Relevant Azure, Databricks, Microsoft Fabric, or cloud certifications are advantageous.
- Experience
- 5+ years' experience in Data Engineering, Analytics Engineering, or Data Warehousing.
- Experience designing enterprise data models and building cloud-based analytics solutions.
- Experience working within Agile project environments.
- Consulting experience is advantageous.
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Technical Skills
- Essential
- SQL and Python
- Data Vault and Dimensional Modelling
- ETL/ELT development
- Azure Data Factory
- Azure Databricks
- Microsoft Fabric
- Azure Synapse Analytics
- Power BI
- Data Warehousing
- Data Governance and Quality Frameworks
- Advantageous
- Snowflake
- Apache Spark
- Microsoft Purview
- Azure DevOps
- DataOps and CI/CD
- Streaming technologies (Kafka/Event Hub)
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Key Competencies
- Analytical thinking and problem-solving
- Strong stakeholder engagement and communication
- Attention to detail and data quality
- Collaborative and client-focused mindset
- Delivery-focused within Agile environments
- Governance and compliance awareness
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Success Measures
- Delivery of trusted, analytics-ready datasets.
- Implementation of scalable and reusable data models.
- Adherence to enterprise data governance and quality standards.
- Successful support of reporting, analytics, and AI initiatives.
- Contribution to high-quality client delivery and data product development.
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