ADF ADB MS Fabric opportunity with Hexaware

2 weeks ago

Bengaluru Chennai Pune, India Hexaware Technologies Full time ₹ 9,00,000 - ₹ 12,00,000 per year

Role & responsibilities

Mandatory skill set : MS FABRIC, ADF ADB

Responsibilities:

Data Integration: Design, develop, and maintain data integration solutions using Microsoft Fabric, ensuring seamless data flow across various systems.

Data Modeling: Create and manage data models within Microsoft Fabric to support analytics and reporting needs.

ETL Processes: Develop and optimize ETL (Extract, Transform, Load) processes to ensure data quality and consistency.

Collaboration: Work closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver solutions.

Performance Tuning: Monitor and optimize the performance of data pipelines and workflows within Microsoft Fabric.

Documentation: Document data integration processes, architectures, and workflows for future reference and compliance.

Best Practices: Implement best practices for data engineering and ensure adherence to data governance policies.

Troubleshooting: Identify and resolve issues related to data integration and performance.

Qualifications:

Education: Bachelors degree in Computer Science, Information Technology, or a related field.

Experience: Proven experience in data engineering, specifically with Microsoft Fabric and related technologies.

Programming Skills: Proficiency in programming languages such as SQL, Python, or C#.

Cloud Platforms: Experience with Microsoft Azure and its data services.

Data Warehousing: Familiarity with data warehousing concepts and tools, particularly within the Microsoft ecosystem.

Version Control: Experience with version control systems like Git.

Communication Skills: Strong verbal and written communication skills to collaborate with cross-functional teams.

Preferred Skills:

Experience with Power BI for data visualization and reporting.

Knowledge of machine learning frameworks and libraries.

Familiarity with CI/CD practices for data pipelines

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