Senior/Principal Azure Data Engineer

Job Description Summary:
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design. A Data Engineer designs data products and data pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost effective.

As a Data Engineer, you will:
• Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric).
• Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage).
• Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions.
• Optimize data pipelines in the Azure environment for performance, scalability, and reliability.
• Ensure data quality and integrity through data validation techniques and frameworks.
• Develop and maintain documentation for data processes, configurations, and best practices.
• Monitor and troubleshoot data pipeline issues to ensure timely resolution.
• Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge.
• Manage the CI/CD process for deploying and maintaining data solutions.

Requirements:
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience) and able to demonstrate high proficiency in programming fundamentals.
• Proven experience as a Data Engineer or similar role dealing with data and ETL processes.
• Strong knowledge of Microsoft Azure services, including Azure Data Factory, Azure Synapse, Azure Databricks, Azure Blob Storage and Azure Data Lake Gen 2.
• Experience utilizing SQL DML to query modern RDBMS in an efficient manner (e.g., SQL Server, PostgreSQL).
• Strong understanding of Software Engineering principles and how they apply to Data Engineering (e.g., CI/CD, version control, testing).
• Experience with big data technologies (e.g., Spark).
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.