Analytics Engineer III – AI & Data Engineering

Client: Boston University
Location: Boston, MA
Duration: 9 Months Contract
Work Arrangement: Remote / Hybrid collaboration
Job Category: Analytics Engineering / Data Engineering / AI

Job Overview

Sigma Systems is seeking an experienced Analytics Engineer III for a 9-month contract opportunity supporting Boston University.

This position offers an opportunity to design, develop, and maintain scalable data models, semantic layers, and data pipelines that transform institutional data into trusted, AI-ready data products.

The ideal candidate will have strong hands-on experience with SQL, Python, dbt, data modeling, data architecture, and cloud data platforms. The role involves applying software engineering best practices to modern data technologies such as Dagster, Apache Iceberg, Trino, and AWS.

The Analytics Engineer will also contribute to AI-driven analytics initiatives by developing structured metadata, semantic models, and business context that enable Large Language Models (LLMs) and AI agents to accurately interpret and query university data.

Candidates with experience in analytics engineering, modern data lakehouse architecture, data pipeline development, and AI/ML data solutions are encouraged to apply.

Key Responsibilities

• Design, develop, test, and maintain scalable analytics-ready data models and transformations using dbt, SQL, and Python.

• Build and maintain data pipelines that support enterprise reporting, analytics, and AI applications.

• Implement semantic layers, business logic, and enterprise metrics to establish consistent and trusted data definitions.

• Develop metadata, documentation, and structured business context that enables LLMs and AI agents to interpret and query institutional data accurately.

• Support AI-driven analytics initiatives involving LLMs, Retrieval-Augmented Generation (RAG), embeddings, and text-to-SQL capabilities.

• Build and support orchestrated data pipelines using modern data engineering tools.

• Deliver curated datasets, machine learning features, and embedding pipelines for AI and analytics applications.

• Implement data quality testing, data contracts, and observability practices to ensure data accuracy and reliability.

• Apply software engineering best practices, including Git version control, code reviews, automated testing, and CI/CD.

• Collaborate with data engineers, AI engineers, analysts, faculty, and business stakeholders to translate requirements into reliable data solutions.

• Support data governance, metadata management, privacy, and security standards.

• Troubleshoot and optimize data models, transformations, and pipeline performance.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics, or a related technical discipline, or equivalent professional experience.

• 5+ years of professional experience in Analytics Engineering, Data Engineering, Business Intelligence Development, or a related technical field.

• Advanced SQL and Python programming skills.

• Hands-on experience developing data models and transformations using dbt or similar data transformation tools.

• Strong understanding of data modeling, data architecture, and semantic layer implementation.

• Experience working with cloud data platforms and modern lakehouse architectures.

• Knowledge of Git, code reviews, automated testing, CI/CD, and pipeline orchestration.

• Experience developing and maintaining reliable production data pipelines.

• Ability to analyze complex technical requirements and independently deliver solutions.

• Understanding of data quality, data governance, privacy, and security best practices.

• Strong communication and collaboration skills.

Preferred Qualifications

• Experience with AWS cloud services, particularly Amazon S3.

• Hands-on experience with Apache Iceberg and Trino.

• Experience with Dagster or similar data orchestration platforms.

• Experience with dbt Core or dbt Cloud.

• Knowledge of semantic modeling, metrics frameworks, and metadata management.

• Experience supporting AI and machine learning initiatives involving:

  • Large Language Models (LLMs)

  • Retrieval-Augmented Generation (RAG)

  • Embeddings and vector databases

  • Text-to-SQL applications

  • AI-driven analytics

  • AI agents and contextual data retrieval

• Experience implementing data contracts, data observability, and automated data quality testing.

• Familiarity with feature engineering and machine learning data pipelines.

• Experience working with enterprise or institutional datasets.

Technical Skills & Keywords

SQL | Python | dbt | Analytics Engineering | Data Engineering | Data Modeling | Data Architecture | Semantic Layer | Data Transformation | Data Pipelines | AWS | Amazon S3 | Apache Iceberg | Trino | Dagster | Git | CI/CD | Automated Testing | Code Reviews | Pipeline Orchestration | Data Quality | Data Contracts | Data Observability | Data Governance | Metadata Management | Cloud Data Platforms | Lakehouse Architecture | Artificial Intelligence | AI Analytics | LLM | Large Language Models | RAG | Retrieval-Augmented Generation | Embeddings | Text-to-SQL | Machine Learning | Feature Engineering

Ideal Candidate Job Titles

• Analytics Engineer III

• Analytics Engineer

• Senior Analytics Engineer

• Data Engineer

• Senior Data Engineer

• dbt Developer

• Analytics Developer

• Business Intelligence Developer

• Data Platform Engineer

• AI Data Engineer

• Cloud Data Engineer

• Data Modeling Engineer

Contract Details

• Position: Analytics Engineer III

• Client: Boston University

• Duration: 9 Months

• Location: Boston, MA

• Work Arrangement: Remote / Hybrid collaboration

• Experience Required: 5+ Years