About the job
Summary: As a Data Engineer at Barnes & Thornburg, you will play a critical role in enhancing our backend data engineering capabilities. Your primary focus will involve the development and upkeep of robust extract, transform, load (ETL) processes to seamlessly integrate data from diverse sources into our proprietary data systems. You will leverage Microsoft Fabric extensively and collaborate closely with data architects, fellow engineers, and analysts to ensure that our data pipelines operate with maximum efficiency and reliability. Furthermore, you will support teams in delivering insightful data dashboards and reporting, thus facilitating the provision of essential information throughout the organization. This position is vital in developing and maintaining our overall data taxonomy, ensuring its consistency across multiple platforms.
Key Responsibilities:
Design, construct, and maintain efficient ETL processes to integrate substantial amounts of legal data securely from multiple sources.
Develop innovative data lake and data warehouse solutions, implementing industry best practices for data ingestion, storage, and retrieval.
Establish and deploy comprehensive data governance and security frameworks, including data access controls and compliance measures.
Work collaboratively with cross-functional teams, including analysts, attorneys, and IT professionals, to grasp data requirements and design backend solutions tailored to their needs.
Evaluate and prioritize data initiatives based on business objectives and best practices to ensure alignment with strategic goals.
Design and implement testing strategies for data pipelines, validating data integrity, accuracy, and performance throughout the workflow.
Create and maintain comprehensive documentation for data architectures, pipelines, transformations, and processes to ensure transparency and facilitate knowledge sharing.
Build and optimize automated data workflows to streamline ingestion, transformation, and processing, thus reducing manual effort and enhancing efficiency.
Optimize data storage and retrieval processes to improve performance and scalability, utilizing cloud-based technology such as Azure.
Contribute to data quality enhancement planning and implementation for new projects.
Stay abreast of the latest industry trends and best practices in data engineering and analytics, continuously evaluating and integrating new tools and techniques to bolster our data infrastructure.
Provide technical support and troubleshooting assistance as needed.

