Senior Analytics Engineer

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Full-Time DENVER, CO, US

3 days ago Requisition

Join our growing team and discover why Summit Utilities, Inc. continues to earn national and regional recognition as an employer of choice. Our recognitions include Best Places to Work in Maine (2019–2025);
Best Places to Work in Arkansas (2020, 2023, 2025);
Best Places to Work in Oklahoma (2022–2025);
Best Places to Work in Missouri (2023 and 2026);
Best Places to Work in Colorado (2025);
Forbes America’s Best Small Employers (2023); and, most recently, Proud and Purposeful Employer (2026).

Summit is a growing natural gas utility providing safe, reliable, and clean burning natural gas service to homes and businesses in Arkansas, Colorado, Maine, Missouri, Oklahoma, and Texas. Being part of the Summit team means embracing excellence and innovation, committing to safety each and every day, and doing all that we can to serve each other, our customers, and the communities where we live.

We aim to bring warmth and energy to everything we do.

We have an exciting hybrid opportunity for a Senior Analytics Engineer based in Denver, CO (SGL
19).

Summit Utilities is seeking a Senior Analytics Engineer to lead the design, development, and operational excellence of enterprise-grade data products that power AI and analytics initiatives across the organization. Engineers at this level set the technical direction for dbt model architecture and the organization’s semantic layer on modern cloud data platforms, prepare and product ionize the trusted, well-governed datasets that AI and machine learning products depend on, lead complex data investigations, and drive data quality and integrity improvements.

The Senior Analytics Engineer is a formal mentor for junior Analytics Engineers, partners with Data Engineers, Data Architects, and AI/ML teams on platform standards, and approves data quality standards for production. Ideal candidates bring expert-level SQL, advanced dbt expertise (macros, Jinja templating, custom generic tests, semantic layer/metrics definitions, CI/CD integration), strong data modeling proficiency, and a track record of building AI-ready data products.

Because the role is domain- and project-agnostic, it applies equally to full-time and contract engagements across any project or business area.

• Lead the design, development, and ongoing maintenance of complex dbt models and transformation pipelines that deliver trusted, AI-ready datasets for analytics and machine learning products.
• Define dbt testing strategies—including custom generic tests, advanced packages (, ), and source freshness SLAs that ensure the accuracy and reliability AI and analytics use cases require.
• Lead in-depth data investigations and drive data integrity improvements through lineage analysis, validation processes, and root cause remediation across upstream and downstream systems.
• Define key metrics, reporting standards, and a governed semantic layer, partnering with data scientists, analysts, and business stakeholders to translate needs into governed, reusable data products that serve both analytics and AI use cases.
• Serve as a formal mentor for junior Analytics Engineers—through dbt pair-programming, code review, and design sessions—and may direct the work of contractors, while partnering with Data Engineers, Data Architects, and AI/ML teams on platform standards, CI/CD, and roadmap

• Bachelor’s degree in data Analytics, Information Systems, Computer Science, Mathematics, or a related field preferred; equivalent combination of education and relevant experience will be considered.
• Extensive experience (typically 10+ years) in analytics engineering, data engineering, BI development, or a related data discipline
• Proven track record designing, building, and operating dbt projects in production, including macros, Jinja templating, custom generic tests, semantic layer/metric definitions, and CI/CD integration.
• Expert SQL with performance tuning, plus experience…