Azure Data Engineer

Overall Experience: 6 to 12 Years

Job Location: Bangalore/Chennai

Job Requirements*

• Azure data factory, Databricks, synapse, delta lake development with hands-on coding experience
• Implement ETL solution to integrate, transform and load data from various sources into data lake and data warehouse
• Hands-on expertise in python, pyspark and sql for large scale data processing
• Optimize and tune data pipelines for performance and scalability
• Ability to write complex SQL queries
• Collaborate with business analysts and business stakeholders to gather requirements and ensure data quality and availability.
• Good understanding of Agile Methodologies and DevOps Culture
• Strong Problem-solving skills

.

Key Responsibilities*

• Design, develop, and deploy scalable data pipelines using Databricks (PySpark, Spark SQL), Azure Synapse, Azure Data Factory, and other Azure data services.
• Implement ETL/ELT processes to ingest, transform, and load data from various sources into data lakes and data warehouses.
• Optimize and tune data pipelines for performance and scalability.
• Write and optimize complex SQL queries for data extraction, transformation, and analysis.
• Use PySpark for large-scale data processing and analytics.
• Implement data partitioning, bucketing, z-ordering, liquid clustering and indexing strategies for efficient data retrieval.
• Integrate data from multiple sources, including structured, semi-structured, and unstructured data.
• Work with APIs, streaming data, and batch processing to ensure seamless data integration.
• Implement data governance practices to ensure data quality, consistency, and security.
• Monitor and troubleshoot data pipelines to ensure data accuracy and availability.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
• Work closely with DevOps teams to deploy and monitor data pipelines in production environments.
• Document data pipelines, workflows, and processes for knowledge sharing and future reference.
• Maintain up-to-date documentation on data architecture and data models.