Job Description
Key Responsibilities
Design, build, and maintain scalable data pipelines that extract, transform, and load data from multiple sources into centralized platforms such as data warehouses, lakehouses, and operational databases.
Develop and manage end-to-end data workflows using Microsoft Fabric, including integration with Azure Data Factory and related Azure data services.
Implement physical and logical data models that support efficient storage, performance, and analytical use cases, while maintaining data integrity and reliability.
Transform raw and semi-structured data into analytics-ready datasets through cleansing, standardization, and deduplication processes.
Integrate data from disparate systems and ensure consistency, accuracy, and quality across the data pipeline.
Optimize database and pipeline performance through tuning, monitoring, and proactive issue resolution.
Collaborate with analytics, reporting, and business teams to translate data requirements into robust technical solutions.
Required Competencies
Bachelor's degree in Computer Science, Information Technology, Mathematics, or a related field.
At least 6 years of progressive, hands-on experience in Data Engineering.
Minimum 6 years of experience in ETL/ELT development, data warehousing, and data modeling.
At least 3 years of experience delivering data solutions on the Microsoft Azure platform, with strong exposure to Microsoft Fabric and Azure Data Factory.
Strong proficiency in PySpark and/or Python for large-scale data processing.
Solid experience with T-SQL and relational database concepts.
Proven expertise in designing, building, and maintaining robust ETL pipelines for structured and semi-structured data in warehouse and lakehouse environments.
Strong understanding of data architecture, performance optimization, and best practices for scalable data platforms.
