Senior Analytics Engineer

Motion Recruitment; Boston, Northern (MA, KY); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Motion Recruitment seeks a Senior Analytics Engineer for a contract-to-hire engagement in the higher education space. You will lead analytics data architecture, semantic modeling, and data pipelines using SQL, Python, dbt, Dagster, and AWS.

This role blends analytics engineering, data engineering, and AI to build scalable data products for reporting, ML, and AI-powered apps. You will own complex data initiatives, define engineering standards, architect semantic layers, and develop context for

Qualifications

• 8+ years in analytics engineering or related field.
• Led complex technical projects and data architecture decisions.
• Expert-level SQL and Python.
• Extensive experience with dbt or similar tools.
• Deep understanding of data architecture and semantic layer design.
• Adv. software engineering practices including Git, testing, CI/CD.
• Experience building scalable data pipelines.
• Knowledge of data quality, governance, privacy, and compliance.
• Experience mentoring engineers and guiding technical decisions.
• Strong collaboration with technical and non-technical stakeholders.

Responsibilities

• Lead analytics data architecture and modeling.
• Define enterprise metrics and semantic layers; enable AI-ready interfaces.
• Design and optimize production data pipelines for analytics and ML.
• Establish engineering standards, testing, CI/CD, and governance.
• Mentor engineers and influence analytics/AI strategy across teams.

Skills

SQL

Python

dbt

Dagster

AWS

Data Modeling

Semantic Layer

Data Pipelines

Data Governance

CI/CD

Mentoring

Communication

Education

Bachelor's in CS/IS/DS

Advanced degree preferred

Tools

Apache Iceberg

Trino

Dagster

ETL Tools

Job description

We are seeking a Senior Analytics Engineer for a contract-to-hire opportunity with a well-established organization in the higher education space. This role will focus on leading the design of analytics data architecture, semantic models, and data pipelines using SQL, Python, dbt, Dagster, and AWS. The position combines analytics engineering, data engineering, and AI to build scalable, trusted data products that support reporting, machine learning, and AI-powered applications.

This is an opportunity for a senior engineer to take technical ownership of complex data initiatives and help shape how an organization uses data and AI. You will define engineering standards, architect semantic layers, and develop the context that allows LLMs and AI agents to accurately interpret and query data. Working closely with data engineers, AI engineers, and business stakeholders, you will also mentor other engineers and influence technical strategy. The role offers hands-on work with modern data technologies, exposure to emerging AI applications, and the opportunity to help build the foundation for future analytics and AI solutions in a collaborative, hybrid environment.

Contract Duration: Contract-to-hire

Required Skills & Experience

• 8+ years of experience in analytics engineering, data engineering, or a related technical field.
• Proven experience leading complex technical projects and making data architecture decisions.
• Expert-level SQL and Python skills.
• Extensive experience building data models and transformations using dbt or similar tools.
• Deep understanding of data architecture, dimensional modeling, lakehouse architecture, and semantic layer design.
• Advanced software engineering practices, including Git, code reviews, automated testing, CI/CD, and pipeline orchestration.
• Experience designing, building, and optimizing scalable data pipelines.
• Strong understanding of data quality, governance, privacy, security, and compliance.
• Experience establishing engineering standards and conducting technical design and code reviews.
• Strong communication skills and experience collaborating with technical and non-technical stakeholders.
• Experience mentoring engineers and providing technical guidance.

Desired Skills & Experience

• Experience with AWS, particularly S3.
• Expertise with Apache Iceberg, Trino, and cloud lakehouse environments.
• Experience with Dagster or similar orchestration platforms.
• Experience designing enterprise semantic layers, metrics frameworks, and metadata management practices.
• Experience enabling AI and ML use cases, including LLM integration, RAG, embeddings, and ML pipelines.
• Familiarity with context engineering, knowledge graphs, MCP, and text-to-SQL applications.
• Experience developing evaluation frameworks to measure the accuracy and reliability of AI-driven analytics.
• Experience implementing data contracts, lineage, observability, and enterprise data quality frameworks.
• Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field; advanced degree preferred, or equivalent experience.

What You Will Be Doing

Tech Breakdown

• SQL, Python, and dbt for analytics data architecture and modeling
• AWS, S3, Apache Iceberg, and Trino for cloud lakehouse solutions
• Dagster and AWS for production-grade data pipelines
• Semantic layers, metadata, MCP, text-to-SQL, LLMs, RAG, and AI-enabled analytics

Daily Responsibilities

• 30% Data Architecture & Modeling: Architect scalable data models and lakehouse layers, establish modeling standards, and resolve complex data design and performance challenges.
• 25% Semantic Modeling & AI Context: Define enterprise metrics, business logic, semantic layers, metadata, and AI-ready interfaces that enable reliable analytics and AI applications.
• 20% Data & ML Pipeline Engineering: Design and optimize production pipelines supporting analytics, feature engineering, model training, scoring, and embedding generation.
• 10% Engineering Standards & Governance: Establish best practices for testing, CI/CD, code reviews, data quality, observability, and data governance.
• 15% Technical Leadership & Collaboration: Lead complex technical initiatives, mentor engineers, collaborate across data and AI teams, and contribute to the organization's analytics and AI strategy.