Senior Analytics Engineer

Senior Analytics Engineer

Location: Boston, MA

Onsite Flexibility: Hybrid

Job Details

• 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.

Job Summary

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.

Key Responsibilities

Analytics Data Architecture & Modeling (30%)

• 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.

Semantic Layer & Context Engineering (25%)

• 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.

Data, AI Analytics & ML Pipeline Engineering (20%)

• 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 monitoring, alerting, and lifecycle management of production pipelines.

Engineering Standards, Quality & Governance (10%)

• Define and champion software engineering and data quality standards for analytics engineering.
• Establish standards for version control, testing, CI/CD, and documentation, and conduct expert-level code reviews.
• Implement enterprise-grade data quality frameworks, data contracts, lineage, and observability.
• Ensure alignment with university data governance, privacy, security, and compliance requirements, including the responsible use of data in AI applications.

Technical Leadership, Collaboration & Mentorship (15%)

• Serve as a technical leader and trusted partner across IS&T and the university.
• Lead technical planning and delivery of complex, cross-functional initiatives, partnering with Data Engineering, AI Engineering, Institutional Research, and business units to align data products with institutional priorities.
• Mentor engineers through design and code reviews, and lead knowledge sharing and team development.
• Evaluate emerging tools and practices and contribute to the roadmap for analytics engineering and AI-ready data within IS&T.

Required Skills

• Expert SQL and Python skills, with extensive experience building data models and transformations using dbt or similar tools.
• Advanced software engineering practices, including Git, automated testing, CI/CD, code review, and pipeline orchestration.
• Deep expertise in data architecture, data modeling, and semantic layer design, including metrics frameworks and metadata management, in cloud lakehouse environments (e.g., S3, Apache Iceberg, Trino).
• Experience enabling AI and ML use cases with data, such as LLM integration, RAG, embeddings, text-to-SQL, or ML pipelines; familiarity with emerging standards such as MCP is desirable.
• Strong understanding of data governance, privacy, security, and compliance for sensitive institutional data.
• Excellent communication skills, with a proven track record of collaborating with technical and non-technical stakeholders, mentoring engineers, and leading technical initiatives.

Education Requirements

• Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics, or a related field (or equivalent combination of education and experience); advanced degree preferred.

Required Experience

• 8 years of professional experience in analytics engineering, data engineering, or a related technical field, including technical leadership of complex projects.

Benefits

• Medical, Vision, and Dental Insurance Plans
• 401k Retirement Fund
• Time Off: In addition to PTO and leave policy, employees have a paid intersession break and 13 paid holidays.
• Retirement: University-funded retirement plan with full vesting after 2 years of eligible service.
• Tuition Assistance Program: Competitive tuition assistance program for yourself and family members.
• Investment in staff personal and professional growth, including lunch and learn sessions and an extensive library of online courses.
• Fun Advisory Board (FAB) events throughout the year and opportunities to engage with peers at NERCOMP and EDUCAUSE events.

About the Client

This global research and teaching institution operates campuses in Boston and runs programs worldwide, employing thousands of professionals across technology, research, academic administration, and student services functions. The organization's Information Services & Technology division supports university-wide operations, research, and innovation, and is actively building its analytics engineering, data engineering, and AI capabilities — creating opportunities for software engineers, data scientists, analytics engineers, and AI specialists to shape the institution's data and AI strategy at scale.

About GTT

GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation, a Native American-owned company in Alaska. We highly value diverse and inclusive workplaces and support Fortune 500 organizations across banking, financial services, technology, life sciences, biotech, utilities, and retail sectors throughout the U.S. and Canada.

Job Number: 26-15639 Industry: Data & Analytics

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