Senior Analytics Engineer

Job Description

Job Description

Senior Analytics Engineer – Boston, MA

Job Type: Full Time
Location: Boston, MA
Work Arrangement: Remote/Hybrid; preference for candidates local to Boston University and able to work onsite as needed

Job Overview

Sigma Inc. is seeking a Senior Analytics Engineer to support a leading higher education environment in Boston, MA. This is a senior-level opportunity for an experienced Analytics Engineer, Data Engineer, Data Architect, or AI/Data Engineering professional who can design scalable analytics data platforms, build governed semantic models, and develop production-grade data pipelines that support reporting, advanced analytics, artificial intelligence (AI), machine learning (ML), and AI agents .

The ideal candidate will combine strong SQL and Python development skills with deep expertise in data modeling, dbt, cloud data architecture, semantic layers, data governance, and modern analytics engineering . This individual will provide technical leadership on complex initiatives while helping shape the organization's evolving data and AI strategy .

Key Responsibilities

• Architect and develop scalable analytics data models, lakehouse layers, and data pipelines using dbt, SQL, and Python .
• Design and govern a centralized semantic layer , including enterprise metrics, business logic, metadata, and reusable data definitions.
• Develop data and metadata foundations that enable LLMs, AI agents, text-to-SQL, and AI-powered analytics to accurately access institutional data.
• Support context engineering and AI-ready data interfaces, including emerging technologies such as MCP (Model Context Protocol) .
• Design and maintain production-grade data, ML, feature engineering, and embedding pipelines .
• Work with Dagster and AWS to orchestrate and deploy scalable data workflows.
• Establish data engineering, software development, testing, and data quality standards .
• Conduct expert-level code reviews and promote engineering best practices.
• Provide technical leadership for complex, cross-functional analytics and AI initiatives.
• Collaborate with data engineering, AI engineering, analytics, and institutional stakeholders.
• Contribute to enterprise analytics, data architecture, and AI strategy .
• Mentor engineers and provide technical guidance without formal supervisory responsibility.
• Help ensure institutional data is handled according to appropriate governance, privacy, security, and compliance standards.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics , or a related technical field; equivalent experience may be considered.
• 8+ years of professional experience in analytics engineering, data engineering, data architecture, or a related technical discipline.
• Demonstrated experience providing technical leadership on complex data or analytics initiatives.
• Expert-level SQL and Python skills.
• Strong experience building analytics data models and transformations using dbt or similar technologies .
• Advanced software engineering experience with:
  • Git
  • Automated testing
  • CI/CD
  • Code review
  • Data pipeline orchestration
• Deep understanding of:
  • Data architecture
  • Data modeling
  • Semantic layer design
  • Enterprise metrics
  • Metadata management
  • Cloud lakehouse architectures
• Experience with modern cloud/data technologies such as AWS, S3, Apache Iceberg, and Trino .
• Experience supporting AI/ML data use cases , such as:
  • LLM integration
  • RAG
  • Embeddings
  • Text-to-SQL
  • ML pipelines
• Strong understanding of data governance, privacy, security, and compliance .
• Excellent communication and collaboration skills.
• Demonstrated ability to mentor engineers and influence technical direction.

Preferred / Nice-to-Have Skills

Candidates with experience in the following areas are especially encouraged to apply:

• Dagster
• dbt
• AWS
• Apache Iceberg
• Trino
• Semantic layer / metrics frameworks
• Metadata management
• AI agents
• LLM applications
• RAG
• Embedding pipelines
• Text-to-SQL
• MCP / Model Context Protocol
• Machine learning data pipelines
• Data governance in higher education or other sensitive-data environments