Analytics Engineer II

Join our Growing Team and see why Summit Utilities, Inc was named as one of the Fastest Growing Denver Area Private Companies 2019 and 2020;
Best Places to Work in Maine 2019, 2020, 2021, 2022 and 2023; and Best Places to Work in Arkansas 2020 and 2023, Oklahoma 2022 and 2023 and Missouri 2023. Summit was also recently named one of Forbes 2023 America's Best Small Employers.

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 opportunity for an Analytics Engineer II (SGL
17) based in Little Rock, Arkansas. This hybrid role may be based in one of our offices in Little Rock, Fort Smith, or Fayetteville, Arkansas.

Summit Utilities is seeking an Analytics Engineer II to design, develop, test, and maintain enterprise-grade analytics data products that power reporting, regulatory submissions, and operational decision-making across the company. Engineers at this level are expected to work independently on dbt model development within Microsoft Fabric and Azure SQL environments, owning specific subject areas (e.g., billing, AMI, SAP, customer) end-to-end - from source profiling through deployment and monitoring.

This role is essential to delivering trustworthy, well-modeled, and well-tested datasets that bridge raw source data and business analytics. Ideal candidates bring 3-6 years of experience, proficiency in SQL and dbt, an understanding of data governance and metadata management, and the ability to translate analytical requirements into durable, well-documented data products.

• Design, build, and maintain dbt models that transform source data into governed, analytics-ready datasets across one or more subject areas.
• Write and maintain dbt schema tests (, unique, , relationships) and source freshness checks to enforce data quality SLAs.
• Optimize SQL transformations for performance, cost-efficiency (CU usage), and maintainability within Microsoft Fabric and Azure SQL environments.
• Investigate root causes of data discrepancies across upstream and downstream systems, partnering with Data Engineers to remediate pipeline issues.
• Gather and document data requirements from Data Analysts, business partners, and regulatory teams, translating them into dbt model designs.
• Implement and maintain dbt project structure including refs, sources, YAML configs, packages (, ), and incremental materializations.
• Author clear, business-friendly documentation in dbt’s documentation site, including model purpose, column definitions, and lineage notes.
• Partner with Data Engineers on pipeline requirements and collaborate on shared standards for ingestion, modeling, and monitoring.
• Contribute to source control and CI/CD workflows for dbt projects, including pull request review and deployment automation.
• Support testing and validation for pipeline enhancements, schema changes, and source onboarding.
• Mentor Analytics Engineer I peers through pair-programming, code review, and knowledge sharing.
• Participate in team standups, sprint planning, code reviews, and architecture discussions.
• Build working knowledge of utility data domains (billing, AMI, SCADA, SAP, GIS) and the data quality needs of each operational system.

• 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.
• 3-6 years of experience in analytics engineering, data engineering, data analysis, or BI development.
• Hands‑on experience building and maintaining dbt models in a production or near-production environment.
• Strong experience working with relational databases, including query writing, optimization, and stored procedure understanding.
• Demonstrated…