Analytics Engineer (Full-time Hybrid, Morrisville, NC Based)

The Analytics Engineer serves as a critical technical bridge between Data Engineering and Data Analytics, applying rigorous data engineering principles to the analytics development lifecycle. This role is responsible for creating, scaling, and managing high-value data assets (including metrics stores, data marts, and semantic layers) to ensure consistent and reliable data consumption across the organization. By implementing best practices such as code versioning, CI/CD, and automated orchestration, the Analytics Engineer brings software engineering discipline to the analytics space to drive scalable and modernized data initiatives. Working closely within business domains, the Analytics Engineer translates complex business metrics and KPIs into actionable analytical artifacts, empowering the organization to achieve greater success through a mature, data-driven culture.

Analytics Pipeline Development & Orchestration

• →Design, build, and manage scalable analytics pipelines to handle the transition from raw data to analytics-ready assets
• →Develop and maintain complex Directed Acyclic Graphs (DAGs) to ensure logical execution and dependency management of data workflows
• →Perform advanced extract, load and transform processes specifically tailored for the analytics domain, focusing on data cleansing, preparation, and transformation

Semantic Modeling & BI Enablement

• →Architect and manage high-value semantic models using Strategy (BI) and Mosaic technology to ensure a centralized source for business metrics
• →Design and scale critical data assets, including metrics stores, semantic layers, and analytical cubes, to support enterprise-wide self-service analytics

Cloud & Database Management

• →Leverage enterprise data platforms to process large-scale datasets and implement modern data engineering workflows within a cloud environment
• →Perform database management tasks for Strategy (BI) databases, ensuring these environments are optimized and properly integrated with analytics pipelines

AI-Augmented Productivity & Engineering

• →Utilize AI-augmented tools to accelerate code development, documentation, and technical problem-solving 
• →Apply responsible AI practices by carefully crafting prompts and rigorously validating AI-generated code and outputs to ensure accuracy and reliability

Cross-Functional Collaboration & Strategic Support

• →Partner closely with the Data Analytics and Data Science teams to proactively identify, define, and deliver the specific data requirements and datasets they need to succeed
• →Collaborate with the Data Governance team to support the development of secure, compliant, and high-quality enterprise data assets and products
• →Engage with cross-departmental teams, including Quality Management and Medical Economics, to translate their unique business needs into robust analytical solutions and data products

Engineering Best Practices & DevOps

• →Apply software engineering disciplines to the analytics lifecycle, including version control, CI/CD, and automated orchestration to drive scalability and consistency 
• →Integrate directly into domain teams to bridge the gap between data management and business needs, facilitating federated analytics architectures

Education & Experience

Required:

Bachelor’s degree in Computer Science, Data Science, Engineering or related field from an accredited college/university with at least three (3) years designing and operating data analytics pipelines.

• Designing, building, and managing scalable analytics pipelines in a modern data stack (i.e., dbt, dlt) to transform raw data into analytics-ready assets.
• Expertise in developing and maintaining complex Directed Acyclic Graphs (DAGs) to ensure logical execution and dependency management.
• Semantic Modeling: Expertise in architecting and managing high-value semantic layers, metrics stores, and analytical cubes using Strategy and Mosaic technology.
• Proficiency in leveraging Databricks and SQL Server to process large-scale datasets and implement modern data engineering workflows in a cloud environment.
• Proficiency in managing PostgreSQL and SQL Server environments, including schema design, indexing, and performance tuning for analytics pipelines.
• Ability to utilize AI-driven tools like GitHub Copilot to accelerate code development, documentation, and technical problem-solving.
• Proficiency in applying software engineering principles, including Git version control, CI/CD, and automated orchestration to the analytics lifecycle.
• Ability to translate complex business metrics and KPIs into technical artifacts while partnering with Data Science, Governance, Quality Management, and Medical Economics teams.
• Advanced technical skills in SQL and Python for sophisticated data manipulation, integration, and modeling.