Senior Analytics Engineer AI, Automation, & Data Engineering
Boston University
- Location
- Hybrid (Boston, Massachusetts)
- Compensation
- $120k - $168k/yr
- Employment
- Full-time
- Level
- Senior Level
About the Role
Boston University seeks a Senior Analytics Engineer to lead the design of analytics data architecture and semantic models. This role focuses on enabling AI solutions by defining metrics and business logic that allow LLMs to reliably query institutional data.
Skills
Benefits
- Paid holidays
- Retirement plan
- Tuition assistance
Perks
- Hybrid Work
- Professional development
Full job details
Boston University Information Services & Technology (IS&T) is seeking applicants with diverse skills and experience to join our innovative and inclusive community. Join us as a Senior Analytics Engineer and help shape how Boston University turns its data into trusted, AI-ready insight. In this role, you will lead the design of the analytics data architecture, semantic models, and pipelines that power university reporting, analytics, and AI solutions. Working at the intersection of analytics engineering, data engineering, and artificial intelligence, you will define the metrics, business logic, and context that enable large language models (LLMs) and AI agents to reliably understand and query institutional data, and you will bring software engineering rigor to modern data tools such as dbt, Dagster, Apache Iceberg, Trino, and AWS. As part of the AI, Automation, and Data Engineering team, you will report to the [Reporting Manager Title], serve as a technical leader on high-priority initiatives, mentor fellow engineers, and partner with data engineers, AI engineers, researchers, faculty, and staff in a hybrid work environment based in Boston, MA. This is an opportunity to help define an evolving discipline and build the data foundations that support university operations, research, and innovation. You Will: Architect and lead development of scalable analytics data models and lakehouse data layers using dbt, SQL, and Python. Define and govern the semantic layer, including enterprise metrics and business logic, as a single source of truth for reporting and AI. Lead context engineering efforts, designing the metadata, semantic models, and AI-ready interfaces (e.g., MCP, text-to-SQL) that enable LLMs and AI agents to accurately use institutional data, and measure their accuracy. Design production-grade data and ML pipelines, including feature engineering and embedding pipelines, using Dagster and AWS. Set engineering and data quality standards and conduct expert-level code reviews. Lead technical planning and delivery for cross-functional initiatives and contribute to IS&T's analytics and AI data strategy. Mentor engineers and foster a culture of engineering excellence.