magnals - Magna Legal Services - Senior Manager
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 • 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 • 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 $130,000 - $150,000 per year. • An employee’s pay position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, travel requirements, revenue-based metrics, any contractual agreements, and business or organizational needs. The range listed is just one component of the total compensation package for employees.
Responsibilities
• Snowflake Platform • 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. • dbt & Transformation Layer • 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. • Azure Data Ecosystem • 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. • Team Leadership • 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. • Standards & Governance • 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.
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