Analytics Engineer

Analytics Engineer Nuvia Dental Implant Center

• 3.1 Salt Lake City, UT Job Details $70,000
• $85,000 a year 1 day ago Benefits Dental insurance Qualifications Data model design Cloud infrastructure Technical documentation SQL Data Architecture Design (Architecture design skills) Project stakeholder communication Python Dimensional modeling Stakeholder management Database software proficiency Full Job Description Pay
• $70,000
• 85,000 per year Role The Analytics Engineer is responsible for transforming raw and intermediate data into clean, reliable, reporting-ready datasets that power business intelligence and AI-driven initiatives.

This role owns the silver-to-gold data layer — designing and maintaining pipelines, data models, and reporting infrastructure that serve internal teams and stakeholders. The Analytics Engineer works closely with the data team and business customers to ensure data is accurate, well-documented, and structured for both dashboard delivery and AI enablement use cases. Responsibilities Build and maintain ETL pipelines moving data from silver to gold layers using Python, SQL, and Windmill. Design and implement data models that serve reporting, dashboard, and AI enablement use cases. Own and improve gold-layer data quality — including testing, validation, and documentation. Translate business reporting requirements into reliable, performant data models. Support internal AI initiatives by ensuring data is structured, consistent, and accessible for downstream AI consumption. Collaborate with internal stakeholders to understand data needs and deliver accurate, timely datasets. Support and contribute to our visualization layer (currently Zoho Analytics; additional tools possible). Participate in code reviews and contribute to team data standards and best practices. Monitor pipeline health and proactively address data issues before they impact downstream consumers. Results Reliable, well-tested gold-layer data models that power accurate reporting and dashboards for internal stakeholders. ETL pipelines that run consistently and are easy to maintain, extend, and debug. Data that is structured, clean, and AI-ready — enabling downstream AI and automation use cases. Well-documented data assets that reduce dependency on tribal knowledge and accelerate onboarding. Stakeholder confidence in data accuracy, reflected in active adoption of dashboards and data products. Proactive identification and resolution of data quality issues before they reach end users. Requirements 2-4 years of experience in an Analytics Engineer, Data Engineer, or Senior Data Analyst role. Strong SQL skills — writes clean, optimized queries with a solid understanding of how data warehouses execute them. Python proficiency for data transformation and pipeline work. Experience working within a layered data architecture (bronze/silver/gold or equivalent). Familiarity with workflow orchestration tools (Windmill, Airflow, Prefect, or similar). AWS experience (S3, Glue, Lambda, or similar services). Solid understanding of data modeling concepts — dimensional modeling, grain, and slowly changing dimensions. Strong documentation habits — writes for the next person, not just themselves. Effective communication skills with the ability to translate technical concepts for non-technical stakeholders. Nice to Have Experience with dbt or similar transformation frameworks. Exposure to Zoho Analytics or other BI/visualization platforms. Familiarity with AI/ML workflows — understanding how clean, structured data feeds LLMs, RAG pipelines, or ML models. Experience building data products with AI enablement in mind (feature stores, semantic layers, structured outputs for LLM consumption). Experience in healthcare, dental, or services industries.