Senior Analytics Engineer

Job Description

Job Description

Senior Analytics Engineer – Data Architecture, dbt, SQL & AI

Job Title: Senior Analytics Engineer
Job ID: 1006
Location: Boston, MA
Duration: 12 Months Contract
Work Arrangement: Remote/Hybrid – Preference for candidates local to Boston who can come onsite as needed

Position Overview

Sigma Inc. is seeking an experienced Senior Analytics Engineer to lead the design and development of modern analytics data architecture, semantic models, and data pipelines supporting business intelligence, advanced analytics, artificial intelligence, and machine learning .

This senior-level role operates at the intersection of analytics engineering, data engineering, data architecture, and AI . The ideal candidate will bring deep expertise in SQL, Python, dbt, data modeling, semantic layers, cloud lakehouse environments, data pipelines, and software engineering .

The Senior Analytics Engineer will serve as a technical lead on complex, high-priority initiatives, establish analytics engineering standards and best practices, and collaborate with data engineering, AI engineering, and business stakeholders to shape modern data and AI solutions.

This position does not have formal supervisory responsibilities but will provide technical leadership, mentorship, and guidance to other engineers .

Key Responsibilities

• Architect and lead the development of scalable analytics data models and lakehouse data layers using dbt, SQL, and Python .
• Define and govern the semantic layer , including enterprise metrics and business logic, as a trusted source for reporting and AI applications.
• Lead context engineering initiatives involving metadata, semantic models, and AI-ready interfaces such as MCP and text-to-SQL .
• Design solutions that enable LLMs and AI agents to accurately interpret and use organizational data.
• Develop and support production-grade data and machine learning pipelines , including feature engineering and embedding pipelines.
• Work with Dagster and AWS to build and orchestrate scalable data workflows.
• Establish engineering, testing, and data quality standards .
• Conduct expert-level code reviews and promote software engineering best practices.
• Lead technical planning and delivery for complex, cross-functional data initiatives.
• Contribute to organizational analytics and AI data strategy .
• Mentor engineers and promote a culture of technical and engineering excellence.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics , or a related field, or equivalent education and experience.
• Advanced degree is preferred.
• 8+ years of professional experience in analytics engineering, data engineering, data architecture, or a related technical field.
• Demonstrated experience providing technical leadership on complex projects .
• Expert-level SQL and Python skills.
• Extensive experience building data models and transformations using dbt or similar tools .
• Advanced software engineering experience with:
  • Git/version control
  • Automated testing
  • CI/CD
  • Code reviews
  • Pipeline orchestration
• Deep expertise in data architecture, data modeling, and semantic layer design .
• Experience with metrics frameworks and metadata management .
• Strong experience with cloud-based lakehouse environments , including technologies such as:
  • Amazon S3
  • Apache Iceberg
  • Trino
• Experience enabling AI and machine learning use cases through data engineering and analytics solutions.
• Strong understanding of data governance, privacy, security, and compliance for sensitive data.
• Excellent communication and collaboration skills.
• Demonstrated ability to mentor engineers and lead technical initiatives involving both technical and non-technical stakeholders.

Preferred Qualifications

• Experience integrating Large Language Models (LLMs) with enterprise data.
• Experience with Retrieval-Augmented Generation (RAG) .
• Experience with embeddings and vector-based data workflows .
• Experience with text-to-SQL solutions.
• Experience building and supporting machine learning pipelines .
• Experience with Dagster .
• Strong hands-on experience with AWS .
• Familiarity with emerging AI standards and technologies such as Model Context Protocol (MCP) .
• Experience developing AI-ready semantic and contextual data layers.