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Data Engineer Key Responsibilities: - Collaborate with clients and internal stakeholders to design and implement scalable data engineering solutions using the Microsoft Analytics ecosystem.
- Develop and maintain data pipelines, ensuring efficient extraction, transformation, and loading (ETL) processes for structured and unstructured data.
- Apply best practices in dimensional modeling to create robust and scalable data models for analytics and reporting.
- Leverage tools such as Python, dbt, and SQL to develop advanced data transformation and integration processes.
- Optimize data storage and retrieval mechanisms to support high-performance analytics workloads.
- Implement and manage hybrid analytics workloads using Azure Cloud infrastructure, including networking and security configurations.
- Monitor and troubleshoot data pipelines and workflows to ensure accuracy, reliability, and performance.
- Collaborate with analytics and BI teams to ensure seamless integration of data assets into Power BI and other reporting platforms.
- Stay informed about emerging trends and technologies in data engineering and cloud analytics to enhance solution delivery.
Required Qualifications: - Proven experience in data engineering with expertise in the Microsoft Analytics stack (Fabric, Synapse, SQL, SSAS, SSRS).
- Strong proficiency in SQL, Python, and data modeling techniques.
- Working knowledge of data extraction patterns and best practices.
- Experience with ETL tools and frameworks, such as dbt.
- Solid understanding of dimensional modeling and its application in analytics.
- Strong problem-solving skills with a focus on delivering scalable and efficient solutions.
- Excellent communication and collaboration skills, with experience in client-facing roles.
Preferred Qualifications: - Experience with Azure Cloud infrastructure, including hybrid analytics workload configurations and networking.
- Certifications in Azure Engineering (e.g., Azure Data Engineer Associate, Azure Solutions Architect Expert).
- Experience with Azure DevOps or GitHub for code repositories and deployment.
- Familiarity with advanced analytics tools and concepts, including machine learning workflows.
- Knowledge of additional data visualization tools such as Tableau or Qlik.
- Experience with Agile methodologies for project management.
Date Posted: 26 March 2025
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