Job Summary
We're opening eyes, hearts and minds to the impact that a pharmacy team can have in changing lives.
Join our group of talented, committed team members-pharmacists, pharmacy care coordinators, technologists, product strategists and more-to create and expand the delivery of personalized health support that people didn't even know could be possible.
The Senior Analytics Engineer for Stellus Rx will help our communities thrive as a key member of the Technology Team. You will work closely with Stellus Rx leaders and across the organization as we work collaboratively to unlock the health of millions of Americans by turning "use as prescribed" into a guarantee, not a direction. We are a culture that is unabashedly driven by purpose — making a difference to our patients and team members while growing at an accelerated rate.
In this evolved role, you will leverage AI-powered tooling, MCP-enabled integrations, and prompt-based development as core parts of your day-to-day workflow — accelerating how clean, reliable data gets transformed into actionable insights and business-ready reports.
Accountabilities:
- Data Quality & Preparation - The engineer is accountable for taking raw data from data engineers and ensuring it is clean, well-organized, and compliant with data hygiene best practices — essentially making data trustworthy and ready for use.
- Data Modeling & Transformation Designing and maintaining meaningful data structures (using Kimball dimensional modeling methodology) that give end users the context they need to answer their own business questions without needing engineering support.
- BI & Reporting Delivery Translating business requirements into finished reports, dashboards, and visualizations — and increasingly doing so efficiently through prompt-based development workflows with AI tools.
- Integration & Pipeline Ownership Building and maintaining integrated views of data from multiple sources, including MCP-enabled connections between data warehouses and BI tools like Qlik or Tableau.
- Documentation & Data Governance Maintaining consistent definitions, schemas, and documentation across the data team so everyone speaks the same data language — a critical accountability in a regulated healthcare environment.
- AI Tooling Adoption Actively using and championing AI-powered tools within their own workflow, and sharing those practices with teammates to lift the team's overall productivity.
- Stakeholder Enablement & Training Bridging the gap between technical data systems and business users — training analysts, pharmacists, coordinators, and other stakeholders to be self-sufficient with data tools.
- Analytics Project Leadership Leading analytics projects end-to-end, from scoping and design through to operationalization, including building project plans and managing milestones.
Role and Responsibilities:
Core Analytics Engineering
- Take data compiled by data engineers and clean it in compliance with data hygiene best practices
- Organize and transform data in a meaningful way, providing additional context to make it ready for analysis
- Work with data engineers to streamline upstream processes so that data is cleaner earlier in the pipeline
- Maintain documentation related to datasets and analysis, ensuring consistent language and definitions across the data team
- Build and maintain complex databases in partnership with the technology team
- Create integrated views of data collected from multiple sources
- Develop and use tools, algorithms, and processes for data mining and data visualization to generate decision-ready reports
- Train business analysts and key stakeholders to use various data tools effectively
- Discover opportunities for the organization to improve systems, enterprises, and processes through the use of data analytics
AI-Assisted Development & Automation Tooling
- Use AI-powered tools and automation platforms (e.g., Claude, ChatGPT, GitHub Copilot, or similar) as active parts of the engineering workflow — accelerating data transformation, SQL development, pipeline troubleshooting, and documentation
- Apply AI tooling to automate repetitive tasks such as data profiling, schema documentation, anomaly flagging, and test case generation
- Continuously evaluate and adopt emerging AI tools that improve the speed, quality, and consistency of analytics engineering work
- Share AI productivity practices with teammates to raise the overall capability of the data team
MCP-Enabled BI Integrations
- Configure and maintain MCP (Model Context Protocol) connections between data sources and BI/visualization tools such as Qlik, Tableau, or equivalent platforms
- Ensure MCP-connected data contexts are well-structured, performant, and aligned with organizational data governance standards
- Collaborate with BI developers and data consumers to troubleshoot, optimize, and expand MCP-based integrations as reporting needs evolve
- Document MCP configurations and integration patterns to support team scalability and knowledge transfer
Prompt-Based BI & Report Development
- Use conversational AI tools (e.g., Claude, ChatGPT) to accelerate the development of BI reports, dashboards, and data visualizations through prompt-based workflows
- Translate business requirements into well-structured prompts that produce accurate SQL, data models, chart specifications, and report logic
- Iterate on and refine AI-generated outputs — validating against source data, applying domain knowledge, and ensuring results meet accuracy and compliance standards
- Build and maintain a library of reusable prompts for recurring reporting needs (e.g., adherence reporting, patient risk summaries, formulary analysis) to promote consistency and efficiency
- Document prompt-to-report workflows so that patterns can be shared, reviewed, and reused across the data team
Qualifications and Requirements:
- Positive and solution-oriented mindset
- Comfort working in a highly agile, intensely iterative environment
- Self-motivated and self-managing, with strong task and project organizational skills
- Great communication: regularly achieve consensus among technical and business teams
- Demonstrated capacity to communicate complex technical requirements and recommendations clearly and concisely
- Extensive experience in the healthcare and pharmaceutical industry
- Demonstrated experience in one or more business subject areas: marketing, finance, sales, product, customer success, customer support, engineering, or people operations
- 6+ years in the data space as an analyst, engineer, scientist, or equivalent
- 4+ years designing, implementing, operating, and extending commercial Kimball enterprise dimensional models
- 4+ years working with a large-scale Data Warehouse in a cloud environment
- 2+ years building reports and dashboards in a data visualization tool
- 1+ years actively using AI tools (LLMs, code assistants, or AI-powered automation) as part of a professional data or analytics workflow
- 1+ years working with MCP-based or equivalent connector/integration configurations within BI platforms
- 1+ years using prompt-based development techniques to generate or accelerate production-quality SQL, data models, or reports
- 1+ years creating project plans to identify tasks, milestones, and deliverables
- Demonstrated experience leading 4+ analytics projects from inception to operationalization
- Demonstrated experience designing and socializing Entity Relationship Diagrams and reference SQL scripts to scale data acumen and adoption
- Experience working with multiple cloud data warehouses, ETL tools, and data visualization technologies
- Bilingual in Spanish and English
Preferred Experience:
- Experience with AWS, Snowflake, Talend, Tableau, or Qlik
- Hands-on experience using Claude, ChatGPT, GitHub Copilot, or similar AI assistants in a data engineering or analytics context
- Familiarity with MCP server/client configurations within Qlik or comparable BI tools
- Background in healthcare data standards (HL7, FHIR, NCPDP) and data privacy requirements in regulated environments
- Prior experience documenting and socializing AI-assisted development workflows with a broader team
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