Analytics Coordinator

Ashland, Oregon | In office four days per week, one remote

Reliable data systems do not stay reliable by accident.

A failed pipeline, broken source, or unnoticed quality issue can ripple into reporting and decisions across the business. We're looking for an Analytics Coordinator who wants to learn how production data systems work end to end and help keep them dependable day after day.

You'll work closely with our analytics and IT teams, monitor data and reporting pipelines, complete recurring data pulls, troubleshoot issues, and strengthen the checks and documentation that help people trust the data they use.

Can you spot that something is off, work the problem methodically, and know when to fix it yourself versus escalation?

If that sounds like your kind of challenge, keep reading.

• Keep the Data Moving. Monitor ETL, analytical, and reporting pipelines; review run logs and data-quality metrics; and flag failed runs or unexpected results quickly.
• Be the First Line of Troubleshooting. Investigate routine pipeline and data-quality issues, document what you tried, resolve what you can, and elevate cleanly when deeper support is needed.
• Own Recurring Data Pulls. Complete scheduled manual extracts accurately and on time, and maintain clear procedures so recurring work is dependable and repeatable.
• Improve Data Quality. Work with senior teammates to implement validations and corrections, surface issues in raw data sources, and strengthen quality monitoring over time.
• Keep the Map Current. Maintain data catalogs, lineage notes, procedures, and system documentation so people can understand how information moves through the system.
• Make the System Easier to Operate. Notice recurring failures, manual friction, or unclear handoffs and contribute practical ideas for better controls, automation, and operating routines as your knowledge grows.
• Support Data Across the Business. Partner with analytics, IT, and other users of company data to clarify issues, communicate status, and keep work moving when something needs attention.

• Methodical Problem Solving. You break technical problems into steps, test what is happening, and avoid guessing when the evidence points somewhere else.
• Detail Awareness. You notice failed runs, unusual values, missing outputs, and other small signs that something may not be working as expected.
• Technical Curiosity. You want to understand how systems, scripts, data sources, and reports connect instead of treating each task as an isolated checklist item.
• Reliable Follow-Through. If you own a recurring pull, open issue, or troubleshooting step, you keep track of it and make sure it reaches a clear outcome.
• Clear Documentation. You can write down what happened, what you tried, and what still needs attention so the next person can pick up the work without starting over.
• Learning Velocity. You are comfortable asking questions, learning from more experienced teammates, and taking on more technical ownership as your capability grows.

• Exposure to at least one programming language through coursework, personal projects, internships, or professional work.
• Experience troubleshooting software or technical systems, including academic or personal-project experience.
• Proficiency with Excel or Google Sheets and comfort working carefully with structured data.
• Comfort with basic statistics, algebra, and Boolean logic.
• Clear written communication and the ability to document technical work in a way another person can follow.
• A technical foundation in computer science, IT, data analytics, mathematics, or a related area built through education, self-directed learning, projects, or hands‑on experience.

You'll have a head start if you've worked with Python or R, SQL and databases, Git, Bash/Linux or other command-line tools, data pipelines, or AWS. Experience with data-quality monitoring, APIs, automation, or data catalogs is also useful.

None of those tools is a substitute for the fundamentals. We're more interested in whether you can learn a system, troubleshoot carefully, document what you know, and become someone the team can trust with day-to-day operations.

This role is designed to build real operating depth in data and analytics systems. You'll start by learning the data lifecycle, monitoring production workflows, completing recurring pulls, handling first-line troubleshooting, and maintaining the documentation that makes the system easier to operate.

As your…