Azure Data Engineer

Job Description

Description We are seeking a skilled Azure Data Engineer with 6-11 years of experience to join our dynamic team. The ideal candidate will be responsible for designing, building, and maintaining data solutions on the Azure platform. You will work closely with cross-functional teams to ensure the efficient processing and management of data, while also driving data-driven decision-making within the organization. If you are passionate about data and have a strong technical background in Azure services, we would love to hear from you. Responsibilities Design and implement data solutions using Azure services such as Azure Data Factory, Azure Databricks, and Azure SQL Database. Develop and maintain data pipelines for data ingestion, transformation, and storage. Ensure data quality and integrity by implementing data validation and cleansing processes. Collaborate with data scientists and analysts to understand data requirements and provide data access. Optimize performance of data processing and storage solutions in Azure. Monitor and troubleshoot data workflows and pipelines to ensure reliability and efficiency. Implement security and compliance measures for data handling and storage. Document data architecture and processes for future reference and onboarding. Skills and Qualifications Bachelor's degree in Computer Science, Information Technology, or a related field. 6-11 years of experience in data engineering or a related field. Strong experience with Azure Data services (Azure Data Factory, Azure Databricks, Azure Synapse Analytics). Proficiency in SQL and experience with relational databases (e.g., Azure SQL Database, SQL Server). Knowledge of programming languages such as Python or Scala for data processing and ETL tasks. Experience with data modeling and database design principles. Familiarity with big data technologies (e.g., Apache Spark, Hadoop) and data warehousing concepts. Understanding of data governance and best practices in data security. Experience with CI/CD processes and DevOps practices for data solutions.