Digital Intelligence Intern

**This is a fully remote position; however the selected candidate must live in Colorado and have the ability to travel to Vail during their internship a handful of times**

Ready to put your data skills to work on real problems Eagle River Water and Sanitation District is looking for a Data Engineering Intern to support our Analytics Intelligence Supervisor in turning raw operational data into reports dashboards and data products that help ERWSD make better decisions while supporting our mission of protecting water and the environment.

If youve worked with SQL Power BI Python and Excel and youre curious about how data flows from source systems to dashboards in a real organization this role is for you. Youll write queries build and maintain Power BI reports wrangle data with Python and Power Query and help capture the lore behind our data (the business rules and institutional knowledge that make numbers meaningful) into semantic models and documentation that anyone can find and trust. We dont expect you to be an expert on day one; were looking for curiosity attention to detail and a willingness to ask good questions.

By the end of this internship youll have hands-on experience writing production SQL building reports business users actually rely on working with version control and modern Python tooling and contributing to data engineering work in a live environment plus the satisfaction of knowing your work supports critical services in our community.

The Day to Day

• Reporting & Analytics (Power BI SQL)
• Help build maintain and refine Power BI datasets reports and dashboards used by departments across the District.
• Write T-SQL queries (joins filters aggregates window functions) to pull shape and validate data from SQL Server source systems.

Semantic Modeling & Data Knowledge Capture

• Help build and maintain semantic layers in Power BI (and adjacent tools such as SSAS Tabular or Microsoft Fabric semantic models) that translate raw source data into business-meaningful entities measures and dimensions.
• Work with senior staff and subject matter experts to capture the lore behind the data the institutional knowledge business rules edge cases and historical context that explain what the data actually means and convert it into reusable documented models.
• Help expose semantic models for natural-language and self-service exploration (e.g. Power BI Q&A paginated reports Excel-connected models) to broaden access to trustworthy data.

Data Engineering & Pipelines

• Assist senior staff with data quality checks profiling and reconciliation between source systems and reporting layers.
• Help maintain and document data pipelines that move and transform data for analytics and reporting.
• Apply version control (Git) to SQL Python and report assets; follow team branching and review conventions.

Scripting & Automation

• Write scripts to clean transform and analyze data sets.
• Use virtual environments and standard scripting tooling to keep work reproducible.
• Where appropriate help integrate scripting work into recurring data processing or reporting workflows.

Documentation & Collaboration

• Help create and maintain documentation for datasets reports queries and pipelines including data definitions and known caveats.
• Participate in team meetings demos and reviews; share progress and learn from other team members.
• Maintain a positive attitude and work cooperatively with the IT/Analytics team District employees vendors and contractors.

We dont expect you to arrive knowing every tool listed here. Any combination of coursework projects labs or work experience that demonstrates analytical ability and a foundation in data is qualifying. If youre strong in some areas and still building in others apply anyway.

Strong candidates will have some experience with:

• T-SQL and relational databases for querying and shaping data
• Python or R for data processing or analysis
• Power BI for reporting and data modeling
• Excel including Power Query and pivot tables
• Git or another version control system
• At least one modern code or query editor (VS Code SSMS Jupyter etc.)

Exposure to any of the following is a plus:

• DAX measures and semantic modeling concepts
• Data pipeline tools such as SSIS or Azure Data Factory
• Azure data services or data lake concepts
• CI/CD concepts and pipelines
• Introductory statistics or basic machine learning
• Documentation systems such as wikis Markdown or SharePoint

Preferred Certifications (Not Required)

Microsoft and other vendor certifications such as:

• PL-300: Microsoft Power BI Data Analyst
• DP-900: Microsoft Azure Data Fundamentals
• DP-203: Microsoft Azure Data Engineer Associate
• DA-100 / PL-300 study or coursework or equivalent analytics credentials
• DP-600: Fabric Analytics Engineer Associate
• DP-700: Fabric Data Engineer Associate

Schedule

This position will work up to a maximum of 1000 hours or six months. We prefer a candidate who is available to work 30 or more hours a week for a six-month time period but will consider alternative schedules. This internship will work mostly remote with travel to Vail required every few weeks. The ideal start date for this internship is in May or June 2026.

Compensation

This role is a paid internship with the pay range of $23.72 $33.22/hour. We value life-work balance and are leaders in the industry with our seasonal employee benefits package which includes but is not limited to:

• $522/month Employee Housing Stipend (Local employees only)
• Wellness program
• 457 Retirement savings plans
• Paid Holidays and PTO
• $800 Annual Recreation Benefit

To Apply

• All District employees must submit to a pre-employment drug screen and extensive background check.
• For a full classification specification email
• All applicants must apply online by May 18 2026 in order to be considered.

Equal Opportunity Employer

We do not discriminate based on race color religion national origin sex age disability sexual orientation marital status genetic information or any other status protected by law or regulation. It is our intention that all qualified applicants be given equal opportunity and that selection decisions are based on job-related factors.