Azure data engineer

2 days ago

Delhi, India Truelancer.com Full time
Job Title: Data Engineer - Fabric
Experience: 5-7 Yrs
Mandatory Skills: ADF, Databricks, Fabric, Python, SQL
Required Technical Skills
Microsoft Fabric
Data warehouse and lakehouse methodologies including medallion architecture
Data ETL/ELT processes
Data profiling and anomaly detection
Data modeling (Dimensional/Kimball)
SQL expertise
Python experience (expertise not required, but would be nice)
Azure Databricks
Azure Data Factory
Power BI
Required Skills & Qualifications:
Proven experience as a Data Engineer, with a focus on data pipeline design, ETL, and data integration.
Strong proficiency in Azure Data Factory (ADF) for creating and managing data pipelines.
Experience with Databricks for building, optimizing, and deploying data pipelines and machine learning models.
Expertise in Microsoft Fabric for managing and integrating data solutions across the Azure ecosystem.
Proficiency in Python, with the ability to write clean, efficient, and reusable code.
Strong experience in writing and optimizing SQL queries for complex data manipulation and analysis.
Hands-on experience with cloud platforms (preferably Azure), data lakes, and data warehousing concepts.
Familiarity with data modeling, data warehousing principles, and database technologies.
Experience with version control (e.g., Git) and CI/CD pipelines for data engineering tasks.
Strong problem-solving skills, attention to detail, and ability to work independently and in a team environment.
Excellent communication skills and the ability to work effectively in a collaborative environment.
Key Responsibilities:
Design, build, and maintain scalable and efficient data pipelines using Azure Data Factory (ADF), Databricks, and Microsoft Fabric.
Develop and implement ETL processes to collect, transform, and load data from various sources into data warehouses and data lakes.
Write complex SQL queries and Python scripts to manage, analyze, and manipulate large datasets.
Optimize data workflows, troubleshoot pipeline issues, and improve overall system performance.
Collaborate with data scientists, analysts, and other engineering teams to understand data requirements and deliver efficient data solutions.
Ensure data quality, integrity, and security in all stages of the data pipeline.
Monitor and optimize data storage, processing, and retrieval performance across cloud and on-premise platforms.
Stay up-to-date with industry trends and advancements in data engineering tools and best practices.

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