Azure Data Engineer

Overall Experience: 6 to 12 Years

Job Location: Bangalore/Hyderabad/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

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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.