Senior Manager - Data Engineering

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 Director of Data Engineering to lead the continued development and maturation of our enterprise data platform. We have made meaningful investments in our Snowflake-based architecture and dbt modeling layer, and we are looking for a hands-on technical leader who can accelerate that momentum—raising the quality bar across our data models, expanding coverage into new business domains, and establishing the architecture, standards, and engineering practices that will support Magna’s continued growth.

As Director of Data Engineering, you will lead a team of data engineers while owning the technical roadmap for our cloud data stack across Snowflake, Azure, and dbt. You will partner closely with Analytics, Product, Operations, and other business stakeholders to translate business needs into reliable, scalable, and well-modeled data assets that teams can trust and build upon.

This is intentionally a hands-on leadership role. We are looking for a Director who can set platform strategy and develop engineers while remaining deeply connected to the technology. The ideal candidate is equally comfortable reviewing a dbt PR, designing a new data model, troubleshooting a pipeline, optimizing a Snowflake workload, leading an architecture discussion, and running a team planning session.
Primary Responsibilities

Snowflake Platform

- Serve as the internal technical authority on Snowflake architecture, performance tuning, cost governance, and security, including RBAC, data masking, and network policies.

- Remain hands-on with Snowflake architecture and optimization, including investigating performance issues, reviewing query patterns, evaluating warehouse configuration, and identifying opportunities to improve cost and scalability.

- Design and maintain a scalable, well-documented warehouse structure, including database, schema, and object hierarchy standards that engineers can consistently apply as new domains and workloads are introduced.

- Drive thoughtful adoption of Snowflake capabilities such as Dynamic Tables, Snowpark, data sharing, Cortex, and emerging functionality.

dbt & Transformation Layer

- Own the dbt project end-to-end and remain actively involved in its development and evolution.

- Establish and enforce modeling conventions, testing strategies, documentation standards, and CI/CD practices.

- Design and maintain a layered modeling architecture (staging → intermediate → marts) that downstream teams can confidently consume and self-serve.

- Regularly review dbt pull requests and provide technical guidance around SQL, modeling decisions, incremental strategies, testing, performance, and maintainability.

- Personally participate in the design of complex or business-critical data models when appropriate.

- Partner with Analytics to establish trusted definitions and reusable models for key business metrics.

Azure Data Ecosystem

- Lead the architecture, development, and operation of data pipelines on Azure, including Azure Data Factory.

- Maintain sufficient hands-on involvement to troubleshoot pipeline failures, review pipeline designs, and guide engineers through complex ingestion and orchestration challenges.

- Ensure reliable and observable data movement from source systems into Snowflake with clear SLAs, monitoring, alerting, retry strategies, and failure recovery.

- Establish scalable patterns for integrating n