The Data Analytics Engineer transforms data into clean, reusable, and business-ready datasets that power enterprise reporting, analytics, and AI initiatives. This role bridges the gap between data engineering and data analytics, owning the semantic layer and enabling scalable governed insights across the organization. This candidate must be research oriented, possess strong analytical and problem-solving skills, be able to effectively communicate the value of company data to leaders at all levels, and have experience leading the development of solutions while working with multiple contributors. This person will work closely with managers and technical teams to understand business objectives, contribute to the data strategy, and design solutions that drive data informed outcomes.

Qualifications:

The ideal candidate will hold a bachelor’s degree in a relevant field and possess at least five years of experience in data modeling, analytics, and semantic layer development. Proficiency in SQL, Python, DAX, or other coding languages is required, along with hands-on experience working with Microsoft Fabric, Power BI, SSMS, SSAS, and medallion architecture. A strong understanding of dimensional modeling, semantic consistency, and data governance is essential. Candidates should also have experience implementing CI/CD best practices, managing deployment pipelines, and utilizing version control systems. Familiarity with AI and machine learning data preparation, as well as feature engineering, is highly desirable.

Principal Responsibilities:

• Architect, design, and maintain silver and gold layer datasets using medallion architecture principles while optimizing data pipelines for stability, scalability, and performance.
• Design, develop, build and optimize semantic models in Microsoft Fabric and SSAS for use with Power BI and other reporting tools. Solve complex semantic and analytics problems with a deep understanding of dimensional modeling.
• Collaborate with Data Platform to ensure clean, reliable Silver-layer data, advise on the bronze layer design, help debug data pipeline issues and perform data quality checks.
• Influence technical strategy and decisions within the analytics teams. Partner with Power BI Developers and Data Analysts to develop business rules, advanced analytics, visualizations, reporting and insights.
• Lead data initiatives end-to-end and act as a technical liaison between analytics, engineering, and business teams. Support AI/ML initiatives by translating business requirements to technical requirements, performing feature engineering and dataset preparation.
• Design scalable, performant data pipelines, implement and enhance CI/CD and version control processes to promote highly efficient and effective release cycles.
• Contribute to data governance, document data lineage, semantic meaning of key metrics, and help maintain data dictionaries and glossaries to help enable self-service.
• Stay current on industry best practices and evolving data platform capabilities to help ensure consistency, performance, integrity, and scalability of analytics solutions.
• Act in accordance with FBT policies and procedures as set forth in the employee handbook.
• Adhere to compliance procedures and attend required compliance training.