Senior Analytics Engineer - Lima, Peru

Job Description

Job Description

Description:

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