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. This position is full-time hybrid. Selected candidate must come into the Home office in Morrisville on Wednesdays, for business meeting as needed, reside within 50 miles of Morrisville North Carolina. Responsibilities & Duties Analytics Pipeline Development & Orchestration Design, build, and manage scalable analytics pipelines to handle the transition from raw data to analytics-ready assetsDevelop and maintain complex Directed Acyclic Graphs (DAGs) to ensure logical execution and dependency management of data workflowsPerform 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 metricsDesign 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 environmentPerform 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 succeedCollaborate with the Data Governance team to support the development of secure, compliant, and high-quality enterprise data assets and productsEngage 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 Minimum Requirements 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. Knowledge, Skills, & Abilities 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. Employment for this position is contingent upon a satisfactory background check and credit check, which will be performed after acceptance of an offer of employment and prior to the employee's start date. Salary Range $83,510-$106,475/Annually Exact compensation will be determined based on the candidate's education, experience, external market data and consideration of internal equity. An excellent fringe benefit package accompanies the salary, which includes: Medical, Dental, Vision, Life, Long Term DisabilityGenerous retirement savings planFlexible work schedules including hybrid/remote optionsPaid time off including vacation, sick leave, holiday, management leaveDress flexibility