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 new source systems and acquired businesses into the enterprise data platform.

Technical Leadership & Architecture

• Own the technical roadmap for the Data Engineering function while remaining actively engaged in architecture and implementation.
• Make and document key architectural decisions across Snowflake, dbt, Azure, data modeling, ingestion, orchestration, governance, and platform reliability.
• Evaluate technical tradeoffs and make pragmatic decisions that balance scalability, reliability, engineering effort, cost, and business needs.
• Proactively identify technical debt and platform investment opportunities and establish a roadmap for addressing them alongside the team.

Team Leadership

• Manage, mentor, and grow a team of data engineers through regular 1:1s, performance management, technical coaching, and career development.
• Own workforce planning, hiring, onboarding, performance management, and career development for the Data Engineering function.
• Build a culture where strong engineering fundamentals, accountability, collaboration, and continuous learning are expected.
• Use code reviews, architecture discussions, pairing, and hands-on technical coaching to raise the capabilities of the team.
• Establish planning and prioritization processes that balance platform investment, technical debt, reliability, and business delivery.

Business Partnership

• Partner closely with Analytics, Product, Operations, Technology, and other business stakeholders to understand their objectives and translate them into scalable data solutions and well-scoped, prioritized engineering work.
• Participate early in business conversations to help stakeholders determine what data and capabilities are needed—not simply fulfill downstream requests.
• Translate complex technical concepts, architecture decisions, risks, and tradeoffs for non-technical stakeholders and senior leaders.

Standards, Quality & Governance

• Define and enforce organization-wide ETL/ELT best practices, naming conventions, documentation requirements, and code review standards.
• Champion data quality, observability, lineage, and ownership across the platform.
• Establish automated testing and quality gates, including dbt tests, freshness monitoring, reconciliation, and other appropriate controls.

Desired Background

Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
• 10+ years of progressive data engineering in production environments.
• Demonstrated success operating as a hands-on engineering leader/player-coach who remains technically involved while managing a team.
• Deep, production-grade Snowflake expertise, including personally designing or significantly evolving warehouse architectures, diagnosing performance issues, optimizing queries and workloads, managing costs, and implementing enterprise security controls.
• Extensive dbt experience, including building and maintaining projects at scale, designing data models, reviewing code, establishing testing and documentation standards, and implementing CI/CD.
• Hands-on Azure Data Factory experience designing, building, operating, or troubleshooting production data pipelines.
• Advanced SQL skills and strong proficiency with Python for pipeline development, automation, and data engineering.
• Strong understanding of dimensional modeling, modern ELT architecture, data warehousing, data quality, lineage, observability, and governance.
• Proven experience hiring, managing, mentoring, and developing data engineers, including 1:1s, performance management, goal setting, and career development.
• Experience owning or significantly influencing the technical roadmap for a production data platform.
• Strong communication skills with the ability to move comfortably between detailed technical conversations with engineers and strategic discussions with non-technical stakeholders and senior leadership.
• Demonstrated ability to translate ambiguous business requirements into scalable technical solutions and prioritized engineering work.

Preferred Qualifications

• Familiarity with data observability tooling such as Elementary, Monte Carlo, or similar.
• Exposure to Snowflake Cortex, Snowpark ML, or other AI/ML capabilities within Snowflake.
• Experience in a high-growth, acquisitive, or scale-up environment where data platforms and engineering standards were built or significantly matured.

Originally posted on Himalayas