Senior Analytics Engineer

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