> Markdown version of [/jobs/ext/4421931-analytics-engineer-dbt-sql-python](https://www.wearedevelopers.com/jobs/ext/4421931-analytics-engineer-dbt-sql-python). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - dbt/SQL/Python - **Company:** Motion Recruitment Partners LLC. - **Location:** Boston, MA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Apache HTTP Server, Application Integration Architecture, Business Logic, Cloud Database, Software Quality, Code Review, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Transformation, Graph Database, Python (Programming Language), Machine Learning, Meta-Data Management, Operational Databases, Software Construction, SQL Databases, Cloud Platform System, Feature Engineering, Sql Optimization, Large Language Models, Generative AI, Agentic-AI, Data Strategy, Git, Data Layers, Containerization, Kubernetes, Information Technology, Machine Learning Operations, Software Version Control, Data Pipelines - **Published:** October 8, 2026 - **Apply:** https://www.dice.com/job-detail/77ea546f-8c0f-4b8f-aab4-57f6fc0c851f ## About the Role * Bachelor's degree in Computer Science, Data Science, Information Systems, Analytics, or a related field, or equivalent experience * 8+ years of experience in analytics engineering, data engineering, or a related technical discipline * Advanced SQL and Python development skills * Extensive experience with dbt or similar data transformation frameworks * Strong expertise in data modeling, data architecture, and semantic layer design * Experience building scalable cloud-based data platforms and lakehouse architectures * Hands-on experience with software engineering best practices including Git, testing, CI/CD, and code reviews * Experience designing and supporting production data pipelines and orchestration frameworks * Knowledge of data governance, privacy, security, and compliance practices * Proven ability to lead complex technical initiatives and collaborate across technical and non-technical teams * Excellent communication, mentorship, and stakeholder management skills Desired Skills & Experience * Advanced degree in a related technical field * Experience with Apache Iceberg, Trino, AWS, or similar cloud data technologies * Knowledge of Dagster or other workflow orchestration platforms * Experience supporting AI and machine learning use cases through data engineering * Familiarity with large language models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, or text-to-SQL solutions * Exposure to semantic knowledge models, metadata management, or knowledge graph technologies * Familiarity with emerging AI integration standards and frameworks, Applicants must be currently authorized to work in the US on a full-time basis now and in the future. ## Description Working across analytics engineering, data engineering, architecture, and artificial intelligence, you will establish trusted, reusable data products and create the semantic foundation needed for AI agents, machine learning workflows, and business intelligence initiatives. This is a highly visible technical leadership role focused on solving complex data challenges, defining engineering standards, and shaping the future of data and AI platforms. You will collaborate closely with engineers, architects, analysts, researchers, and business stakeholders while serving as a technical mentor and subject matter expert on strategic initiatives. Required Skills & Experience, * Architect and develop scalable analytics-ready data models and enterprise data products * Design and implement modern lakehouse architectures using cloud-native technologies * Establish and govern semantic layers, business metrics, and enterprise business logic * Build AI-ready data interfaces and context frameworks that enable trusted analytics and AI applications * Design and optimize batch and event-driven data pipelines * Develop feature engineering, model scoring, and embedding pipelines to support analytics and machine learning initiatives * Establish engineering standards for testing, source control, CI/CD, documentation, and code quality * Implement data quality monitoring, lineage, observability, and governance frameworks * Lead technical planning and execution of cross-functional projects * Mentor engineers through architecture reviews, design guidance, and code reviews * Evaluate emerging technologies and contribute to the long-term analytics and AI data strategy