Job
Description
Education: Bachelor’s or master’s degree in computer science, Information Technology, Engineering, or a related field. Experience: 8-10 years 8+ years of experience in data engineering or a related field. Strong hands-on experience with Azure Databricks , Spark , Python/Scala, CICD, Scripting for data processing. Experience working in multiple file formats like Parquet , Delta , and Iceberg . Knowledge of Kafka or similar streaming technologies for real-time data ingestion. Experience with data governance and data security in Azure. Proven track record of building large-scale data ingestion and ETL pipelines in cloud environments, specifically Azure. Deep understanding of Azure Data Services (e.g., Azure Blob Storage, Azure Data Lake, Azure SQL Data Warehouse, Event Hubs, Functions etc.). Familiarity with data lakes , data warehouses , and modern data architectures. Experience with CI/CD pipelines , version control (Git), Jenkins and agile methodologies. Understanding of cloud infrastructure and architecture principles (especially within Azure ). Technical Skills: Expert-level proficiency in Spark, SPARK Streaming , including optimization, debugging, and troubleshooting Spark jobs. Solid knowledge of Azure Databricks for scalable, distributed data processing. Strong coding skills in Python and Scala for data processing. Experience working with SQL , especially for large datasets. Knowledge of data formats like Iceberg , Parquet , ORC , and Delta Lake . Leadership Skills: Proven ability to lead and mentor a team of data engineers, ensuring adherence to best practices. Excellent communication skills, capable of interacting with both technical and non-technical stakeholders. Strong problem-solving, analytical, and troubleshooting abilities.
