Senior Analytics Engineer

Senior Analytics Engineer

Location: Boston, MA

Onsite Flexibility: Hybrid

• Position Type: Direct Placement
• Pay / Salary: $175,000 / Year (USD)
• Work Authorization: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Boston University Information Services & Technology (IS&T) is seeking applicants with diverse skills and experience to join our innovative and inclusive community. The Senior Analytics Engineer leads the design and development of the analytics data architecture, semantic models, and data pipelines that power reporting, analytics, and AI-enabled solutions. Operating at the intersection of analytics engineering, data engineering, data architecture, and artificial intelligence, this role applies advanced software engineering principles to deliver trusted, reusable data products and defines the governed semantic and contextual foundation that enables accurate AI analytics, agents, and machine learning workflows across the university.

The Senior Analytics Engineer serves as a technical lead on complex, high-priority initiatives, establishes standards and best practices for analytics engineering, and partners with data engineering, AI engineering, and institutional stakeholders to shape the university's data and AI strategy. As analytics, data, and AI disciplines continue to converge, this role is expected to help define how analytics engineering evolves within IS&T.

This role does not include formal supervisory responsibilities but provides technical leadership, mentorship, and guidance to other engineers.

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, 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.

• Architect, design, and lead the development of scalable, analytics-ready data models and data products.
• Lead the design of enterprise data models and layered lakehouse architectures using dbt, SQL, and Python on platforms such as S3, Apache Iceberg, and Trino.
• Define modeling patterns and standards that ensure consistency, reusability, and performance, and resolve complex data design and scalability challenges.
• Partner with Data Architects and Data Engineers on data architecture, integration patterns, and platform design decisions.

• Lead the design and implementation of the semantic and context layers that connect university data to people and AI systems.
• Architect and govern the semantic layer, including enterprise metrics, business logic, and entity relationships, as a single source of truth for reporting and AI applications.
• Design context engineering approaches (e.g., semantic models, knowledge graphs, metadata, retrieval strategies) and AI-ready data interfaces (e.g., MCP, text-to-SQL) in partnership with AI Engineering.
• Establish evaluation frameworks to measure and improve the accuracy and trustworthiness of AI-driven analytics.

• Design and deliver production-grade pipelines that support analytics, machine learning, and AI solutions.
• Architect and optimize orchestrated batch and event-driven data pipelines using Dagster and AWS services.
• Design pipelines for feature engineering, model training and scoring, and embedding generation that support ML models, RAG, and AI analytics.
• Ensure operational reliability through…