Lead Data Engineer

- Advanced hands-on experience with Snowflake, including data warehouse architecture, performance tuning, security and access controls, and cost optimization. - Advanced SQL skills and strong experience with a scripting language such as Python for data processing, automation, and troubleshooting. - Strong experience designing and operating scalable ETL/ELT pipelines, including orchestration, testing, monitoring, and production support.

- Own the technical direction, architecture, and ongoing evolution of our Snowflake data warehouse, with accountability for performance, scalability, security, reliability, and cost efficiency. - Lead the design, development, and standardization of scalable ETL/ELT pipelines across data sources and destinations, including the continued evolution toward Azure Data Factory. - Establish and uphold engineering standards for code quality, testing, documentation, version control, deployment, monitoring, and data quality.

- Hands-on experience with dimensional data modeling, star schema and medallion architectures, and dbt or a comparable transformation framework. - Experience with Azure Data Factory or a comparable cloud orchestration platform; experience modernizing or migrating legacy data pipelines is preferred. - Demonstrated technical leadership through mentoring, design and code reviews, engineering standards, and cross-functional delivery, with strong communication and prioritization skills.

- Establish and uphold engineering standards for code quality, testing, documentation, version control, deployment, monitoring, and data quality. - Lead the design and development of dimensional data models, including star schemas within a medallion architecture, using tools such as dbt, Sigma, and related technologies. - Partner with technical and business stakeholders to translate data needs into a prioritized technical roadmap and deliver trusted, reusable data solutions that support decision-making across the company.

Magna Legal Services is a trusted nationwide partner to law firms, corporations, insurance carriers, and government agencies, delivering comprehensive legal support at every stage of a case. From court reporting and record retrieval to jury consulting, investigations, litigation graphics, and language services, we help our clients navigate complex legal challenges with confidence. Our reputation is built on the expertise, dedication, and professionalism of our team, and we’re proud to foster a culture where talented people can do meaningful work and grow their careers. Magna Legal Services is seeking a hands-on Lead Data Engineer to provide technical leadership for the design, development, and ongoing evolution of our enterprise data platform. This role will own the architecture and optimization of scalable ETL/ELT pipelines and our Snowflake data warehouse, establish engineering standards, and guide the delivery of reliable data solutions across the organization. The Lead Data Engineer will partner with technical and business stakeholders, mentor team members, and remain actively involved in implementation as we modernize and scale our data infrastructure. Primary Responsibilities

Primary Responsibilities - Own the technical direction, architecture, and ongoing evolution of our Snowflake data warehouse, with accountability for performance, scalability, security, reliability, and cost efficiency. - Lead the design, development, and standardization of scalable ETL/ELT pipelines across data sources and destinations, including the continued evolution toward Azure Data Factory.

- 5+ years of progressive experience in data engineering, including ownership of complex initiatives and technical leadership within a modern cloud data environment. - Advanced hands-on experience with Snowflake, including data warehouse architecture, performance tuning, security and access controls, and cost optimization. - Advanced SQL skills and strong experience with a scripting language such as Python for data processing, automation, and troubleshooting.

- Strong experience designing and operating scalable ETL/ELT pipelines, including orchestration, testing, monitoring, and production support. - Hands-on experience with dimensional data modeling, star schema and medallion architectures, and dbt or a comparable transformation framework. - Experience with Azure Data Factory or a comparable cloud orchestration platform; experience modernizing or migrating legacy data pipelines is preferred.