Data Engineer
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Job Description
Data Engineer leading transformation of analytics infrastructure for the National Society of Leadership and Success. Collaborating on modern tools and data architecture to impact millions of students.
Responsibilities:
- Contribute to the Snowflake migration: Work with the broader data team to deprecate our legacy Redshift and Apache Hop infrastructure by completing the migration to Snowflake and dbt Cloud
- Build production-grade dbt pipelines: Develop and maintain SQL transformations in dbt that power analytics for business stakeholders across the organization
- Establish data architecture: Refine and maintain our medallion architecture (bronze, silver, and gold layers) to create clear separation of concerns and a single source of truth
- Collaborate with the data team: Partner with our AWS Engineer, Analytics Engineer, and Business Analyst in a sprint-based workflow with code reviews and regular standups
- Maintain dbt transformations (60-70% of role): Own approximately 15-20 core models and summary views that drive business reporting, ensuring they're performant, well-documented, and easy to maintain
- Build new data pipelines: Ingest data from APIs and new sources as business needs evolve
- Enable reverse ETL: Develop and manage batch processes to send transformed data to downstream services like HubSpot using tools like Hightouch
- Monitor data quality: Implement testing and alerting to catch issues before they impact stakeholders
- Contribute to technical standards: Help establish and maintain best practices for code quality, documentation, and data modeling
Requirements:
- 2-5 years of experience as a Data Engineer, Analytics Engineer, or similar role
- Expert SQL skills: You write advanced, readable SQL including CTEs, window functions, and query optimization techniques
- dbt proficiency: You've built and maintained production dbt projects and understand modeling best practices (this is critical for the role)
- Python fundamentals: Comfortable working with dataframes, querying APIs, and writing scripts for data processing
- Modern data stack familiarity: Experience with Snowflake, AWS, and orchestration tools like dbt Cloud or Airflow
- Code craftsmanship: You write clean, modular, well-documented code that others can easily understand and maintain
- AI-assisted development: Proficient with AI coding tools (Claude Code, GitHub Copilot, Cursor, or similar) to accelerate development, debug efficiently, and learn new technologies quickly
Benefits:
- Health insurance
- Professional development opportunities
- Work-life balance: Standard business hours (9-5 or similar), no on-call or off-hours expectations
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