The Analytics Engineer bridges the gap between data engineering and analytics by enriching available data sources, enabling new data products, building semantic models, and improving infrastructure to support scalable and governed analytics solutions. This role is critical in enabling self-service analytics and ensuring data quality and accessibility across SBA.
Requirements
- Advanced proficiency in SQL, DAX, and Power BI, with demonstrated experience building and optimizing semantic models and dataflows.
- Hands-on experience with Microsoft Fabric and modern data architecture principles.
- Strong understanding of data governance, row-level security (RLS), and metadata management.
- Familiarity with CI/CD pipelines, version control systems (e.g., Git), and data orchestration tools (e.g., Azure Data Factory, dbt, or similar).
- Proven ability to translate complex business logic into scalable, reusable data products.
- Minimum of 3 years of experience in analytics engineering, data modeling, or business intelligence development.
Responsibilities
- Design, build, and maintain robust semantic models and curated datasets that translate business logic into reusable, governed data products for self-service analytics and AI consumption.
- Develop and optimize data pipelines and dataflows to support scalable and performant analytics solutions across Microsoft Fabric, Power BI, Azure, and Databricks, with an emphasis on preparing clean, structured data for AI Agentic workflows.
- Implement and manage row-level security (RLS) within semantic models to ensure appropriate data access and compliance with organizational policies.
- Develop and execute testing and validation strategies to ensure the accuracy, reliability, and consistency of data transformations and analytics outputs.
- Apply engineering best practices such as modular design, version control, and code documentation to analytics workflows.
- Document business logic, data transformations, and model structures in technical documentation, data dictionaries, and metadata catalogs, ensuring data lineage and feature clarity to support generative AI and predictive modeling initiatives.
- Design and implement automated data workflows and orchestration strategies to streamline model deployment, ensure timely data availability, and support scalable analytics operations.
Other
- Collaborate with citizen developers and business stakeholders to understand analytical requirements and ensure semantic models and data products meet usability, accuracy, and compliance standards.
- Excellent communication skills and ability to collaborate with both technical and non-technical stakeholders.
- Bachelor’s or Master’s degree in Data Engineering, Computer Science, Information Systems, a related field, or equivalent experience.
- You are a self-starter.
- You are resourceful and thrive in a fast-paced environment.
