Senior Analytics Engineer
Boston – REMOTE
9+ months
Position Summary:
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.
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 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.
Essential Functions:
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 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
