Job Purpose
Design and deliver data transformation pipelines, semantic models, and BI solutions that enable reliable, self-service analytics across the organization. The role bridges data engineering and business intelligence, owning the analytical data layer from source transformation through to governed, business-ready reporting assets.

Key Result Responsibilities
• Design and maintain modular, well-tested ELT pipelines using tools such as dbt, Azure Data Factory, or equivalent orchestration frameworks.
• Build and maintain the semantic/analytical data layer in Snowflake or Microsoft Fabric, including fact and dimension tables, conformed metrics, and reusable dbt models.
• Develop production-grade Power BI reports and dashboards, including well-structured data models, DAX measures, and row-level security configurations.
• Define and implement data quality rules, testing frameworks, and monitoring to ensure accuracy and consistency of analytics outputs.

Key Result Responsibilities-Continued
• Collaborate with data analysts, business stakeholders, and data engineers to translate reporting requirements into robust, governed data assets.
• Apply and enforce dimensional modelling principles (star schema, slowly changing dimensions) to support efficient BI consumption.
• Work within Azure and Snowflake environments to manage datasets, optimize query performance, and control data access.
• Maintain clear documentation for all pipelines, semantic models, metric definitions, and report logic.
• Participate actively in code reviews and contribute to improving team standards for analytics engineering.

Qualifications (Academic, training, languages)
• Bachelor's degree in Computer Science, Information Technology, Business Analytics, Statistics, or a related field.
• Fluent in English Language.
• ITIL Certification is an advantage but not mandatory.
• Strong SQL skills and solid working knowledge of Python for data transformation tasks.
• Working knowledge of dbt for transformation layer development, including tests, documentation, and lineage.
• Hands-on experience with Power BI: data modelling, DAX, report design, and workspace governance.
• Demonstrable experience delivering Power BI solutions in a professional setting.
• Familiarity with Microsoft Fabric or Azure Data Factory for pipeline orchestration and data movement.
• Solid understanding of dimensional modelling (star/snowflake schema, SCD types).
• Understanding of BI governance principles: semantic model management, certified datasets, and access control in Power BI.

Work Experience
• With 2–4 years of hands-on experience in analytics engineering, data engineering, or BI development.
• Experience with Snowflake or Azure Synapse Analytics for data warehousing and query optimization.
• Experience with Git-based version control and collaborative development workflows.