Analytics Engineer II

Summit Utilities Inc•Fayetteville, AR

About The Position

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.

Requirements

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 experience working within cloud-based data environments (Microsoft Fabric, Azure SQL, AWS, or similar).
Familiarity with source control practices and CI/CD workflows for analytics or data projects.
Proficient SQL for querying, transformation, and analysis – comfortable with complex joins, CTEs, window functions, and subqueries
Working knowledge of dbt: ability to develop models, write schema tests, use packages, and understand refs and sources.
Solid understanding of ETL/ELT concepts and the data lifecycle from ingestion through consumption
Familiarity with data governance and metadata management practices
Ability to gather requirements, document assumptions, and translate analytical needs into dbt model designs.
Skilled at root cause analysis and triage across upstream and downstream systems
Working knowledge of cloud data platforms, with hands-on experience in Microsoft Fabric or Azure SQL preferred
Familiarity with API integrations, flat file ingestion (CSV, JSON, XML), and SAP data structures
Ability to work independently and own portions of project delivery from requirements through deployment.
Strong written documentation habits and ability to communicate technical decisions to teammates and stakeholders.
Proficiency with source control tools (Git, Azure DevOps, GitHub) and collaborative development practices
Working knowledge of natural gas distribution and utility data systems (billing, AMI, SCADA), and an understanding of data quality needs across operational systems
Friendly, solution-oriented team player aligned with Summit’s PEAKS values.

Nice To Haves

Master’s degree in a quantitative field is preferred.

Responsibilities

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 (not_null, unique, accepted_values, 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 (dbt_utils, dbt_expectations), 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.