Analytics Engineer III

System One is recruiting an Analytics Engineer III for an unnamed university. The role builds and maintains analytics-ready data models, semantic layers, and pipelines, and provides governed data context for reporting and AI-enabled solutions.

Responsibilities:

• Design, build, test, and maintain analytics-ready data models and transformations using dbt, SQL, and Python
• Implement metrics and business logic within a semantic layer to ensure consistent, trusted definitions across reporting tools and AI applications
• Develop metadata, documentation, and structured context that enables LLMs and AI agents to accurately interpret and query university data
• Build and support orchestrated data pipelines, and deliver curated datasets, features, and embeddings for machine learning and AI analytics
• Implement data quality tests, data contracts, and observability to ensure accuracy and reliability
• Apply software engineering best practices, including version control, code review, automated testing, and CI/CD
• Partner with stakeholders to translate business and research needs into data solutions

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics, or a related field (or equivalent combination of education and experience)
• 5+ years of professional experience in analytics engineering, data engineering, business intelligence development, or a related technical field
• Advanced SQL and Python skills, with experience building data models and transformations using dbt or similar tools
• Experience with software engineering practices, including Git, code review, automated testing, CI/CD, and pipeline orchestration
• Experience with data modeling, data architecture, and semantic layer implementation in cloud lakehouse environments (e.g., S3, Apache Iceberg, Trino)
• Ability to analyze technical requirements and independently deliver complex solutions with minimal oversight
• Strong communication skills and a solid understanding of data governance, privacy, and security best practices
• Exposure to LLMs, RAG, embeddings, or AI-driven analytics