Senior Analytics Engineer

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    "description": "<p><span style=\"font-weight: bold\">Job Summary</span></p>\n<p>We're opening eyes, hearts and minds to the impact that a pharmacy team can have in changing lives.</p>\n<p> </p>\n<p>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.</p>\n<p><br></p>\n<p>The <span style=\"font-weight: bold\">Senior Analytics Engineer</span> for Stellus Rx will help our communities thrive as a key member of the <span style=\"font-weight: bold\">Technology Team</span>. 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.</p>\n<p>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.</p>\n<p> </p>\n<p><span style=\"font-weight: bold\">Accountabilities:</span></p>\n<ul>\n<li>Data Quality &amp; 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.</li>\n<li>Data Modeling &amp; 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.</li>\n<li>BI &amp; Reporting Delivery Translating business requirements into finished reports, dashboards, and visualizations — and increasingly doing so efficiently through prompt-based development workflows with AI tools.</li>\n<li>Integration &amp; 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.</li>\n<li>Documentation &amp; 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.</li>\n<li>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.</li>\n<li>Stakeholder Enablement &amp; 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.</li>\n<li>Analytics Project Leadership Leading analytics projects end-to-end, from scoping and design through to operationalization, including building project plans and managing milestones.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Role and Responsibilities:</span></p>\n<p><span style=\"font-weight: bold\">Core Analytics Engineering</span></p>\n<ul>\n<li>Take data compiled by data engineers and clean it in compliance with data hygiene best practices</li>\n<li>Organize and transform data in a meaningful way, providing additional context to make it ready for analysis</li>\n<li>Work with data engineers to streamline upstream processes so that data is cleaner earlier in the pipeline</li>\n<li>Maintain documentation related to datasets and analysis, ensuring consistent language and definitions across the data team</li>\n<li>Build and maintain complex databases in partnership with the technology team</li>\n<li>Create integrated views of data collected from multiple sources</li>\n<li>Develop and use tools, algorithms, and processes for data mining and data visualization to generate decision-ready reports</li>\n<li>Train business analysts and key stakeholders to use various data tools effectively</li>\n<li>Discover opportunities for the organization to improve systems, enterprises, and processes through the use of data analytics</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">AI-Assisted Development &amp; Automation Tooling</span></p>\n<ul>\n<li>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</li>\n<li>Apply AI tooling to automate repetitive tasks such as data profiling, schema documentation, anomaly flagging, and test case generation</li>\n<li>Continuously evaluate and adopt emerging AI tools that improve the speed, quality, and consistency of analytics engineering work</li>\n<li>Share AI productivity practices with teammates to raise the overall capability of the data team</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">MCP-Enabled BI Integrations</span></p>\n<ul>\n<li>Configure and maintain MCP (Model Context Protocol) connections between data sources and BI/visualization tools such as Qlik, Tableau, or equivalent platforms</li>\n<li>Ensure MCP-connected data contexts are well-structured, performant, and aligned with organizational data governance standards</li>\n<li>Collaborate with BI developers and data consumers to troubleshoot, optimize, and expand MCP-based integrations as reporting needs evolve</li>\n<li>Document MCP configurations and integration patterns to support team scalability and knowledge transfer</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Prompt-Based BI &amp; Report Development</span></p>\n<ul>\n<li>Use conversational AI tools (e.g., Claude, ChatGPT) to accelerate the development of BI reports, dashboards, and data visualizations through prompt-based workflows</li>\n<li>Translate business requirements into well-structured prompts that produce accurate SQL, data models, chart specifications, and report logic</li>\n<li>Iterate on and refine AI-generated outputs — validating against source data, applying domain knowledge, and ensuring results meet accuracy and compliance standards</li>\n<li>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</li>\n<li>Document prompt-to-report workflows so that patterns can be shared, reviewed, and reused across the data team</li>\n</ul>\n<p><span style=\"font-weight: bold\"> </span></p>\n<p><span style=\"font-weight: bold\">Qualifications and Requirements:</span></p>\n<ul>\n<li>Positive and solution-oriented mindset</li>\n<li>Comfort working in a highly agile, intensely iterative environment</li>\n<li>Self-motivated and self-managing, with strong task and project organizational skills</li>\n<li>Great communication: regularly achieve consensus among technical and business teams</li>\n<li>Demonstrated capacity to communicate complex technical requirements and recommendations clearly and concisely</li>\n<li>Extensive experience in the healthcare and pharmaceutical industry</li>\n<li>Demonstrated experience in one or more business subject areas: marketing, finance, sales, product, customer success, customer support, engineering, or people operations</li>\n<li>6+ years in the data space as an analyst, engineer, scientist, or equivalent</li>\n<li>4+ years designing, implementing, operating, and extending commercial Kimball enterprise dimensional models</li>\n<li>4+ years working with a large-scale Data Warehouse in a cloud environment</li>\n<li>2+ years building reports and dashboards in a data visualization tool</li>\n<li>1+ years actively using AI tools (LLMs, code assistants, or AI-powered automation) as part of a professional data or analytics workflow</li>\n<li>1+ years working with MCP-based or equivalent connector/integration configurations within BI platforms</li>\n<li>1+ years using prompt-based development techniques to generate or accelerate production-quality SQL, data models, or reports</li>\n<li>1+ years creating project plans to identify tasks, milestones, and deliverables</li>\n<li>Demonstrated experience leading 4+ analytics projects from inception to operationalization</li>\n<li>Demonstrated experience designing and socializing Entity Relationship Diagrams and reference SQL scripts to scale data acumen and adoption</li>\n<li>Experience working with multiple cloud data warehouses, ETL tools, and data visualization technologies</li>\n<li>Bilingual in Spanish and English</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Preferred Experience:</span></p>\n<ul>\n<li>Experience with AWS, Snowflake, Talend, Tableau, or Qlik</li>\n<li>Hands-on experience using Claude, ChatGPT, GitHub Copilot, or similar AI assistants in a data engineering or analytics context</li>\n<li>Familiarity with MCP server/client configurations within Qlik or comparable BI tools</li>\n<li>Background in healthcare data standards (HL7, FHIR, NCPDP) and data privacy requirements in regulated environments</li>\n<li>Prior experience documenting and socializing AI-assisted development workflows with a broader team<br></li>\n</ul>",
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