Senior Manager - Data Engineering

Job Description

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.

Job Description:

Job Title: Senior Manager - Data Engineering

Position Summary:

This role will continue building and maturing our data platform. We have made meaningful investments in our Snowflake-based architecture and dbt modeling layer, and we are looking for a leader who can accelerate that momentum — raising the quality bar on our data models, expanding coverage across new domains, and establishing the conventions and processes that will carry the platform forward as the business grows.

You will lead a team of data engineers, own the technical roadmap for our cloud data stack (Snowflake, Azure, dbt), and partner closely with analytics, product, and operations stakeholders to turn raw data into reliable, well-modeled assets that teams can trust and build on. This is a hands-on leadership role — the ideal candidate is equally comfortable reviewing a dbt PR, designing a new data model, and running a team planning session.

Key Responsibilities:

Snowflake Platform

dbt & Transformation Layer

Azure Data Ecosystem

Team Leadership

Standards & Governance

• Serve as the internal authority on Snowflake architecture, performance tuning, cost governance, and security (RBAC, data masking, network policies).
• Design and maintain a scalable, well-documented warehouse structure including database, schema, and object hierarchy standards.
• Drive Snowflake feature adoption — dynamic tables, Snowpark, data sharing, and emerging capabilities.
• Own the dbt project end-to-end: modeling conventions, testing strategy, documentation standards, and CI/CD integration.
• Establish and enforce a layered modeling approach (staging → intermediate → marts) that downstream teams can trust and self-serve.
• Lead the design and operation of data pipelines on Azure, including Azure Data Factory
• Ensure reliable, monitored data movement from source systems into Snowflake with clear SLAs and alerting.
• Manage, mentor, and grow a team of data engineers — running regular 1:1s, setting performance goals, and building a culture of engineering excellence.
• Own hiring, onboarding, and career development for the data engineering function.
• Translate business requirements from stakeholders into well-scoped, prioritized engineering work.
• Define and enforce organization-wide ETL/ELT best practices, naming conventions, and code review standards.
• Champion data quality, observability (e.g., dbt tests), and lineage across the platform.
• Proactively identify opportunities for data process improvements and lead initiatives to implement these changes.

Qualifications:

Bachelor’s degree in computer science, Information Technology, Engineering, or a related field

7+ years in data engineering, with at least 2 years in a team lead or management role

Deep, production-grade Snowflake expertise — you have designed warehouse architectures, optimized query performance, managed costs, and implemented enterprise security controls

Fluency with dbt: you have built and maintained dbt projects at scale and can articulate opinionated best practices

Hands-on Azure Data Factory experience

Strong SQL skills and proficiency in Python for data pipeline development and automation

Proven ability to lead and grow a small team while remaining technically engaged

Strong communicator who can translate complex data concepts to non-technical stakeholders and contribute to strategic planning conversations

Nice to have:

Familiarity with data observability tooling (Elementary, Monte Carlo, or similar).

Exposure to Snowflake Cortex, Snowpark ML, or other AI/ML capabilities on Snowflake

Experience in a high-growth or scale-up environment where standards were built from the ground up

Compensation: USD $140,000 - $170,000 per year.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.