Data Engineer role focused on building and optimizing data pipelines within Microsoft Fabric at AIM. Collaborating closely with analysts and developers to deliver scalable data solutions.
Responsibilities
Design, develop, and maintain data pipelines using Microsoft Fabric
Develop and optimize Spark Notebooks (PySpark / Spark SQL) for data transformation and processing
Build scalable ETL/ELT workflows for structured and unstructured data
Work within Microsoft Fabric ecosystem (Data Factory, Lakehouse, Warehousing)
Design and manage data models and storage layers (Bronze, Silver, Gold architecture)
Ensure performance optimization and efficient data processing
Integrate data from multiple sources, including: APIs, Databases, Cloud storage systems
Ensure data quality, validation, and governance best practices
Collaborate with Power BI developers and analysts to support reporting requirements
Enable clean, structured datasets for reporting and visualization
Support business teams with data insights and troubleshooting
Monitor and optimize data pipelines and Spark workloads
Troubleshoot data issues and ensure reliability and performance
Support deployment and CI/CD processes for data solutions
Maintain technical documentation for data pipelines and workflows
Support standardization of data engineering processes and best practices
Requirements
Bachelor’s degree in Computer Science, Data Engineering, IT, or a related field
4–6+ years of experience in data engineering or data platform development
Hands-on experience with Microsoft Fabric or Azure Data Platform
Strong experience working with Spark Notebooks (PySpark / Spark SQL)
Expertise in: Microsoft Fabric, Spark / PySpark / Spark SQL, Data pipelines (ETL/ELT)
Experience with: Azure Data Factory / Synapse / Fabric pipelines, Data Lake / Lakehouse architecture, SQL and relational databases
Understanding of: Data modeling concepts, Data governance and quality frameworks
Benefits
100% remote work environment
Opportunity to work on modern cloud and data platforms (Microsoft Fabric)
Exposure to enterprise-scale data and analytics projects
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