Sr. Analytics Engineer

Dutch Bros Coffee•Phoenix, AZ

2d•$94,500•Onsite

About The Position

At Dutch Bros, we move fast, love what we do, and make an impact that goes beyond the cup. The Senior Analytics Engineer is at the heart of that mission — designing, developing, and optimizing enterprise-grade business intelligence and data solutions that fuel smarter, faster, data-driven decisions across our growing organization. This role blends strong data engineering fundamentals with advanced Power BI development, dimensional modeling expertise, and modern CI/CD deployment practices — all in service of a brand built on speed, connection, and genuine love for the work. The ideal candidate brings deep SQL expertise (Snowflake required), extensive Power BI experience, and a strong foundation in data modeling and warehouse design. But beyond the technical chops, we’re looking for someone who thrives in a fast-paced, high-energy environment, brings a team-first attitude, and genuinely gets excited about turning data into stories that move people — and the business — forward.

Requirements

Bachelor’s degree in: Computer Science, Data Science, or related field (preferred).
6–7 years of Power BI experience (data modeling, DAX, performance tuning, workspace governance).
7–10 years of SQL experience (Snowflake required).
Strong experience in: Data modeling (dimensional modeling, star/snowflake schemas) Data warehousing concepts ETL/ELT design and implementation Building scalable semantic models Hands-on experience implementing CI/CD for Power BI (Git integration, DevOps/ deployment pipelines).
Experience designing and optimizing large datasets for enterprise analytics.
Advanced proficiency in DAX, Power Query (M), and performance optimization techniques.
Strong communication skills — able to present technical concepts to non-technical stakeholders.

Nice To Haves

Hands on experience with assessing and implementing Self-Serve Analytics tools like ThoughtSpot.
1–2 years of ELT process experience (pipeline orchestration, transformation optimization).
1–3 years of Machine Learning experience (predictive modeling, statistical analysis, model integration into reporting workflows).
Experience leveraging Power BI Copilot for report acceleration and automation.
Experience with Python or R for analytics or automation.
Exposure to cloud data architecture and modern data platform design.

Responsibilities

Design, develop, and maintain scalable, enterprise-level Power BI dashboards, reports, and semantic models that deliver actionable insights aligned to business strategy.
Build, optimize, and maintain complex Snowflake-based SQL transformations and data models, ensuring high performance, scalability, and reliability across reporting environments.
Architect and implement robust dimensional data models (star/snowflake schemas) that enable consistent, governed, and reusable analytics solutions.
Implement and manage CI/CD pipelines for Power BI deployments, ensuring version control, automated validation, and seamless promotion across development, test, and production environments.
Partner with Finance, Operations, and cross-functional stakeholders to translate business requirements into functional and technical specifications, delivering solutions on time and to specification.
Ensure consistent, reliable, and streamlined access to trusted data through strong data governance, documentation, testing, and performance optimization practices.
Support and enhance ELT processes, improving data pipeline efficiency, data quality, and transformation performance within Snowflake.
Identify and execute strategies to increase adoption of data consumption solutions across the organization, ensuring stakeholders are engaged and leveraging tools effectively.
Serve as a subject matter expert and trusted advisor to stakeholders, proactively identifying opportunities to improve reporting, automation, and advanced analytics capabilities.
Continuously evaluate and implement emerging technologies (e.g., Power BI Copilot, ML-enhanced analytics) to enhance reporting automation, insight generation, and decision support.
Champion analytics best practices and lead knowledge-sharing initiatives to elevate overall data literacy and technical competency across teams.