Senior Manager – Data Engineering

🕒 May 27

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Magna Legal Services

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2007

🤝 B2B

📚 Education

💰 Debt financing on 2020-02

B2B • Education

Magna Legal Services is a U. S. -based provider of end-to-end litigation support services for law firms, insurance companies, corporations, and government agencies. The company offers court reporting and virtual depositions, record retrieval, service of process, investigative and language services, social media surveillance, jury research and consulting (JuryConfirm, JuryEvaluator, JuryScout), trial presentation and graphics, and related trial support. Magna also produces legal conferences and CLE/CE programs and provides nationwide scheduling and online tools to support litigation from discovery through trial.

📋 Description

• 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.

🎯 Requirements

• 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