Analytics Engineer

RemyCorporation; Boston (MA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

RemyCorporation in the greater Boston area is seeking a Senior Analytics Engineer to lead analytics data model design, semantic frameworks, and data pipelines that support enterprise reporting and data-driven decision making.

The role sits at the intersection of analytics engineering, data engineering, and AI initiatives, with mentorship and architectural guidance. This is a contract opportunity requiring local, hybrid work in the Boston area.

Qualifications

• 8+ years in analytics or data engineering roles.

Responsibilities

• Architect scalable analytics data models and data products.
• Lead development of lakehouse data layers using dbt, SQL and Python.
• Design enterprise semantic models, metrics and business logic for reporting.
• Build and optimize data pipelines for analytics, ML and AI use cases.
• Establish data governance, testing, and observability practices.
• Mentor engineers through reviews and knowledge-sharing.

Skills

SQL

Python

dbt

Data modeling

Data pipelines

Cloud platforms

CI/CD

Git

Stakeholder comms

Education

Bachelor's degree

Tools

dbt

Job description

This is a contract opportunity with our client in the greater Boston area. Must be local and able to work a hybrid schedule.

Please no 3rd party calls

Overview

We are seeking a Senior Analytics Engineer to lead the design and development of scalable analytics data models, semantic frameworks, and data pipelines that support enterprise reporting, analytics, and data-driven decision making.

This role sits at the intersection of analytics engineering, data engineering, data architecture, and emerging AI technologies. The ideal candidate will help build trusted, reusable data products while establishing standards and best practices that enable consistent reporting, advanced analytics, and future AI initiatives.

The Senior Analytics Engineer will serve as a technical leader on complex initiatives, collaborating with engineering teams, business stakeholders, and leadership to shape the organization's analytics strategy. This position provides technical mentorship and architectural guidance but does not include direct people management responsibilities.

Responsibilities

• Architect and develop scalable analytics-ready data models and data products.
• Architect and lead development of scalable analytics data models and lakehouse data layers using dbt, SQL, and Python.
• Design and maintain enterprise semantic models, metrics, and business logic that serve as a trusted source for analytics and reporting.
• Build and optimize data transformation pipelines using SQL, Python, and modern analytics engineering tools.
• Partner with data engineering teams to design lakehouse and cloud data architectures.
• Develop and support data pipelines that enable analytics, machine learning, and AI-driven use cases.
• Establish best practices for data quality, testing, version control, CI/CD, documentation, and governance.
• Implement data observability, lineage, and monitoring capabilities.
• Lead technical design discussions and provide guidance on architecture and engineering standards.
• Mentor engineers through code reviews, design reviews, and knowledge-sharing activities.
• Collaborate with business and technical stakeholders to translate requirements into scalable data solutions.
• Evaluate emerging technologies and contribute to the evolution of the enterprise analytics platform.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field.
• 8+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a related discipline.
• Advanced SQL and Python and ideally dbt or similar tool
• Extensive experience building and maintaining analytics data models and transformation frameworks.
• Strong understanding of dimensional modeling, semantic modeling, and enterprise reporting architectures.
• Experience working in modern cloud-based data platforms and lakehouse environments.
• Expertise with software engineering best practices including Git, automated testing, CI/CD, and code reviews.
• Experience supporting AI, machine learning, or advanced analytics initiatives through data engineering and modeling solutions.
• Strong understanding of data governance, quality, privacy, and security principles.
• Excellent communication skills and the ability to partner with both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with dbt or similar analytics engineering tools.
• Experience with modern orchestration platforms.
• Exposure to semantic layer technologies, metadata management, and metrics frameworks.
• Experience supporting generative AI, LLM, RAG, or machine learning initiatives.
• Experience serving as a technical lead on complex data platform projects.