Data Engineer

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November 15

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Magna Legal Services

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B2B • Enterprise

Magna Legal Services is a nationwide litigation support firm that provides end-to-end services for law firms, insurance companies, corporations and government agencies. The company offers court reporting and virtual depositions, record retrieval, service of process, investigative services, legal interpreting and translation, video and trial presentation/graphics, jury research and consulting, and related courtroom support. Magna also produces legal conferences and CLE programming and provides 24-hour scheduling and nationwide coverage to support clients through discovery, trial preparation and courtroom presentation.

501 - 1000 employees

Founded 2007

🤝 B2B

🏢 Enterprise

📋 Description

• Lead the enhancement and maintenance of our Snowflake data warehouse, ensuring optimal performance, scalability, and security. • Design, implement, and standardize ETL processes, focusing on efficiency and consistency across data sources and destinations. • Evaluate and adopt best practices and tools for data integration, ensuring robust and scalable data pipelines. • Support the building of numerous star schema data models in a medallion architecture utilizing tools like dbt, Sigma, and others. • Work closely with stakeholders to understand data needs, delivering custom data solutions and reports that support decision-making across the company. • Proactively identify opportunities for data process improvements and lead initiatives to implement these changes. • Manage a variety of ad hoc data engineering projects, demonstrating flexibility and problem-solving skills in a rapidly changing environment. • Foster a culture of data literacy and best practices within the team and across the organization.

🎯 Requirements

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. • 3+ years of experience in data engineering, with a strong focus on ETL process optimization and data warehousing, particularly using Snowflake. • Demonstrated ability to design, implement, and standardize ETL processes, with a keen eye for automation and improvement opportunities. • Solid understanding of SQL and experience with scripting languages (e.g., Python) for data processing and automation. • Hands-on experience with data modeling, preferably in a star schema architecture, and general data architecture best practices. • Practical experience with data integration tools and methodologies, with a preference for those familiar with transitioning to Azure Data Factory from other ETL frameworks. • Excellent analytical and problem-solving skills, capable of handling multiple ad hoc tasks in a dynamic and fast-paced environment. • Strong communication and collaboration skills, with the ability to engage effectively with both technical and non-technical stakeholders.