Senior Analytics Engineer
$66,000–$66,000 year
RemoteUnited States or Denver, Colorado, United States
Job Summary
Design, develop, and maintain scalable data models and marts in dbt and Snowflake that support business intelligence across multiple functions. Evaluate architectural tradeoffs related to modeling patterns, performance, and scalability while defining consistent metrics across the Omni semantic layer and downstream reporting tools. Govern semantic layer standards by reviewing model changes for consistency, documentation, and quality, and build AI-enabled tools to expand responsible self-service analytics. Partner with Finance, Marketing, Product, and Sales to translate ambiguous business needs into technical solutions, and own data quality by monitoring performance and resolving discrepancies. Provide technical guidance, code reviews, and onboarding support to reinforce team standards. Submit an updated English resume to be considered.
Required Qualifications
- 5+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a closely related technical field
- Bachelor's degree in Computer Science, Statistics, Information Systems, Data Science, or a related field, or equivalent work experience
- Advanced proficiency in SQL and hands-on experience developing, testing, and maintaining production data models in dbt and Snowflake
- Experience working with semantic layer or business intelligence tools (i.e. Omni, Looker, Tableau) and orchestration tools (i.e. Airflow, Dagster)
- Working proficiency in Python and experience applying software development practices such as version control, testing, code review, and documentation
- Demonstrated ability to leverage AI tools to improve workflows, streamline execution, or enhance outputs
Desired Qualifications
- Strong technical judgment with the ability to transform loosely defined business problems into scalable, maintainable, and well-documented data solutions
- Clear communication skills with the ability to explain technical tradeoffs and business impact to both technical and non-technical stakeholders
- High attention to detail and a consistent commitment to data accuracy, reliability, and quality
- A collaborative approach to technical mentorship, feedback, documentation, and cross-functional problem-solving
- A proactive ownership mindset with the ability to identify risks, resolve issues, and move complex work forward with limited direction
- Exceptional breadth of interest shown through tangible, self-initiated ventures or deep community involvement; you love trying new things and may possess a demonstrated history of successfully pivoting or starting over in life and work
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