Senior Data & Analytics Engineer

Job Summary:
Our Client is looking for a highly skilled Senior Data & Analytics Engineer.  This role is critical in designing and delivering scalable, high-performance data solutions that empower analytics, reporting, and strategic decision-making. The ideal candidate will have expertise in Microsoft T-SQL, Azure Synapse Analytics, Azure Data Factory (ADF) and Power BI semantic layer, along with a solid grasp of insurance data domains such as policy, claims, underwriting, and actuarial.

Key Responsibilities:

• Design, develop, and maintain data marts tailored to the specific needs of the insurance business.
• Develop scalable ETL/ELT pipelines using Fabric Data Factory (Pipelines and Dataflows Gen2) and PySpark Notebooks. Optimize Fabric Spark runtimes for large-scale data processing.
• Strong focus on data integrity, performance tuning, idempotency, cost analysis, testing, and risk mitigation.
• Develop and optimize complex T-SQL queries, stored procedures, and views for high-performance data access.
• Collaborate with the Data Analytics team to deliver actionable insights to the business utilizing Power BI's semantic layer, including custom KPIs using DAX.
• Ensure data quality, consistency, and governance across all data layers.
• Document data models, business logic, and technical specifications to ensure reliability and transparency.
• Stay current with Azure technologies and insurance data trends to drive continuous improvement.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related discipline.
• 5+ years of experience in data engineering, analytics, or BI roles.
• Strong hands-on experience with Microsoft T-SQL, Azure Synapse Analytics, Power BI Semantic Layer (or SSAS) and Azure Data Factory.
• Proficiency in Python/PySpark. Experience with DAX is a strong plus.
• Experience with Git, branch management, and CI/CD pipelines in a data environment.
• Experience with data warehousing, dimensional modeling, and data architecture.
• Proven ability to optimize performance in large-scale data environments.
• Excellent communication and collaboration skills with both technical and business stakeholders.
• Ability to manage concurrent deliverables in a fast-paced environment.