Analytics Engineer

Job Description

• The Analytics Engineer III is responsible for designing, developing, and maintaining the analytics-ready data models, semantic layers, and data pipelines that power reporting, analytics, and AI-enabled solutions at Client. Working at the intersection of analytics engineering, data engineering, and artificial intelligence, this role applies software engineering principles to transform raw data into trusted, well-documented, and reusable data products, and to provide the governed business context that enables accurate AI analytics, agents, and machine learning workflows.

• The Analytics Engineer III works independently on complex tasks, contributes to data architecture and solution design, and collaborates with cross-functional partners across data engineering, AI engineering, and university business units. As analytics, data, and AI disciplines continue to converge, the scope of this role is expected to evolve alongside emerging technologies and institutional needs. This role does not include formal supervisory or leadership responsibilities but may provide knowledge sharing and technical input as part of project work.

Essential Functions:

1. Analytics Data Modeling & Development (35%)

Design, build, test, and maintain analytics-ready data models and transformations that serve as trusted sources for reporting, analytics, and AI.
•Develop modular, reusable data models and transformations using dbt, SQL, and Python in cloud lakehouse environments (e.g., S3, Apache Iceberg, Trino)
•Apply modern data modeling and design techniques (e.g., dimensional, layered/medallion) and contribute to data architecture and design decisions
•Optimize models and queries for performance, cost, and usability, and support enhancements and troubleshooting of existing data assets

1. Semantic Modeling & Context Engineering (25%)

Build and maintain the semantic and metadata layers that give people and AI systems a consistent, governed understanding of university data.
•Implement metrics, business logic, and entity relationships in a semantic layer to ensure consistent definitions across reporting tools and AI applications
•Develop metadata, documentation, lineage, and structured context (e.g., schema descriptions, example queries, domain knowledge) that enables LLMs and AI agents to accurately interpret and query data
•Test the accuracy of AI-driven analytics (e.g., natural language querying, text-to-SQL) and refine context and models to improve results

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

Develop and support pipelines that deliver data to analytics, machine learning, and AI applications.
•Build and maintain orchestrated data pipelines using Dagster and AWS services
•Prepare curated datasets, features, and embeddings that support ML models, RAG pipelines, and AI analytics, in partnership with Data Engineers and AI Engineers
•Monitor and support the operational health of analytics and ML pipelines

1. Engineering Standards, Data Quality & Governance (10%)

Apply software engineering best practices to deliver reliable, secure, and maintainable data products.
•Use version control, code review, automated testing, and CI/CD for all analytics code, and contribute to documentation standards
•Implement data tests, data contracts, and observability to ensure data accuracy and integrity
•Follow established practices for security, privacy, and compliance with university data governance standards

1. Collaboration & Continuous Learning (10%)

Partner with stakeholders and stay current with evolving analytics, data, and AI technologies.
•Partner with faculty, staff, analysts, and technical teams to translate business and research needs into data models, metric definitions, and technical specifications
•Communicate technical concepts and data definitions clearly to a range of audiences
•Evaluate and apply new tools and methods, and participate in professional development opportunities
Posting Description for the HR Website:
Client Information Services & Technology (IS&T) is seeking applicants with diverse skills and experience to join our innovative and inclusive community.
Join us as an Analytics Engineer III where you will design, develop, and maintain the data models, semantic layers, and pipelines that turn university data into trusted, AI-ready data products. Working at the intersection of analytics engineering, data engineering, and artificial intelligence, you will apply software engineering practices to modern data tools such as dbt, Dagster, Apache Iceberg, Trino, and AWS, and help build the business context that allows large language models (LLMs) and AI agents to understand and accurately answer questions about university data. As part of the AI, Automation, and Data Engineering team, you will report to the [Reporting Manager Title] and work closely with data engineers, AI engineers, analysts, faculty, and staff in a hybrid work environment based in Boston, MA.
You Will:
• 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.
Posting Requirements for the HR Website:
You Will Have:
• 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).
• Exposure to LLMs, RAG, embeddings, or AI-driven analytics is desirable.
• 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.