Senior Analytics Engineer
Harnham, Raleigh, NC, United States
Role Overview
We are seeking a Senior Analytics Engineer to join a growing Analytics Delivery organization. This role focuses on designing and owning the semantic and metrics layer that powers business intelligence, self-service analytics, and emerging AI/LLM-driven analytics tools.
This is a highly technical, business-facing role centered on data modeling, transformation, and business logic. It is not a dashboard or report development position. You will operate at the intersection of data engineering, analytics, and business context, ensuring analytical data is scalable, reliable, and interpretable by both humans and AI systems.
Key Responsibilities
Build and maintain dbt data models and transformations
Design, define, and own the semantic and metrics layer
Translate business questions into scalable, well-defined analytics models
Establish clean, consistent, and trusted metric definitions
Structure data to support BI tools, self-service analytics, and AI/LLM-driven querying
Collaborate closely with business stakeholders, BI developers, and data engineering teams
Ensure analytics models follow best practices for scalability, documentation, and reuse
Required Experience and Qualifications
6–8+ years of total professional experience
Background typically includes 3+ years in data engineering and 3+ years in analytics engineering or advanced BI work
Strong healthcare data experience preferred, including medical claims data, EHR/EMR systems, and familiarity with HL7 and FHIR
Understanding of clinical data versus transactional healthcare data
Technical Skills
Advanced SQL skills
Strong hands-on experience with dbt, including macros, snapshots, exposures, and metrics layers
Deep understanding of data modeling principles, semantic layers, and translating business logic into analytics-ready datasets
Nice-to-Have Experience
Snowflake
Databricks or Spark
Looker or LookML
Semantic layer tools such as AtScale or Cube
ThoughtSpot or Sigma, especially for AI-driven or agentic BI
Microsoft Fabric
Prefect, Monte Carlo, or similar data reliability tooling
#J-18808-Ljbffr
We are seeking a Senior Analytics Engineer to join a growing Analytics Delivery organization. This role focuses on designing and owning the semantic and metrics layer that powers business intelligence, self-service analytics, and emerging AI/LLM-driven analytics tools.
This is a highly technical, business-facing role centered on data modeling, transformation, and business logic. It is not a dashboard or report development position. You will operate at the intersection of data engineering, analytics, and business context, ensuring analytical data is scalable, reliable, and interpretable by both humans and AI systems.
Key Responsibilities
Build and maintain dbt data models and transformations
Design, define, and own the semantic and metrics layer
Translate business questions into scalable, well-defined analytics models
Establish clean, consistent, and trusted metric definitions
Structure data to support BI tools, self-service analytics, and AI/LLM-driven querying
Collaborate closely with business stakeholders, BI developers, and data engineering teams
Ensure analytics models follow best practices for scalability, documentation, and reuse
Required Experience and Qualifications
6–8+ years of total professional experience
Background typically includes 3+ years in data engineering and 3+ years in analytics engineering or advanced BI work
Strong healthcare data experience preferred, including medical claims data, EHR/EMR systems, and familiarity with HL7 and FHIR
Understanding of clinical data versus transactional healthcare data
Technical Skills
Advanced SQL skills
Strong hands-on experience with dbt, including macros, snapshots, exposures, and metrics layers
Deep understanding of data modeling principles, semantic layers, and translating business logic into analytics-ready datasets
Nice-to-Have Experience
Snowflake
Databricks or Spark
Looker or LookML
Semantic layer tools such as AtScale or Cube
ThoughtSpot or Sigma, especially for AI-driven or agentic BI
Microsoft Fabric
Prefect, Monte Carlo, or similar data reliability tooling
#J-18808-Ljbffr