Solution Advancement Engineer, Analytics Products

Accountabilities: End-to-End Analytics Product Delivery: Lead the deployment, configuration, and maintenance of healthcare analytics applications across multiple client engagements, overseeing data translation pipelines, application setup, quality control, user acceptance testing (UAT), and final client sign-off. AI Agent Platform Development: Design and implement core capabilities for an AI-powered agent platform, including large language model (LLM) agent loops, subagent architectures, registry and dispatch mechanisms, structured outputs, and evaluation frameworks. Full-Stack Application Engineering: Build and enhance interactive analytics applications using Python and R, particularly Shiny, ensuring reliable functionality, intuitive user experiences, and alignment with client requirements. Deployment Infrastructure and DevOps: Maintain and improve Docker-based deployment infrastructure, manage version tagging and rollback procedures, and support continuous integration and delivery workflows using tools such as Jenkins. Cloud Data Infrastructure: Develop and maintain client data pipelines using cloud-based services, including AWS Athena, S3, and RDS, while supporting reliable data processing and analytics delivery. Client Collaboration and Product Ownership: Work directly with healthcare clients and clinical consultants to understand clinical workflows, reporting requirements, operational metrics, and business needs. Translate stakeholder feedback and UAT findings into product improvements, user experience enhancements, and architectural decisions. Quality Assurance and Performance Optimization: Coordinate comparison testing against existing reporting solutions, identify discrepancies, resolve technical issues, and improve the reliability, performance, and delivery speed of analytics products across concurrent engagements. Architecture and Technical Leadership: Contribute to platform design decisions, establish engineering best practices, and help shape the long-term evolution of analytics and AI capabilities rather than simply implementing predefined specifications. Team Collaboration and Mentoring: Support the development of other engineers through technical guidance, knowledge sharing, and collaboration within a small, autonomous team. Continuous Improvement: Adapt quickly to changing client requirements, prioritize competing deliverables, and identify opportunities to streamline development, deployment, testing, and product delivery processes. Requirements Education: Bachelor's degree in Computer Science, Data Science, Engineering, or a related discipline, or equivalent professional experience. Professional Experience: At least 6 years of software development experience, including demonstrated ownership of a production software product across its full lifecycle. Python Development: Strong proficiency in Python, with the ability to design, build, test, deploy, and maintain production-ready applications. R and Analytics Applications: Working proficiency in R and experience developing interactive analytics applications, particularly with Shiny. AI and LLM Engineering: Practical experience developing LLM-powered or agent-based applications, including familiarity with Pydantic AI, retrieval-augmented generation (RAG), structured outputs, or comparable agent orchestration frameworks. Containerization and Deployment: Hands-on experience with Docker and containerized application deployment, including deployment scripting, version management, rollback procedures, and production support. Product Ownership: Proven ability to manage client-facing analytics or software products end to end, from data pipelines and application interfaces through testing, stakeholder validation, and client approval. Data and Cloud Technologies: Ability to work with data pipelines and cloud infrastructure. Experience with AWS services such as Athena, S3, and RDS is particularly valuable. Client-Facing Communication: Excellent written and verbal communication skills, with the ability to facilitate UAT sessions, understand clinical stakeholder needs, explain technical concepts, and incorporate feedback into product decisions. Problem-Solving and Adaptability: Strong analytical thinking, attention to detail, and a self-directed approach to resolving technical challenges in an environment where requirements may evolve rapidly. Project and Time Management: Ability to manage multiple client engagements simultaneously, prioritize deliverables, adapt to changing requirements, and meet deadlines tied to project milestones. Collaboration and Technical Judgment: Strong interpersonal skills, a collaborative mindset, and the ability to contribute to architectural decisions and engineering standards in a high-autonomy team. Work Authorization: Current authorization to work in the United States is required. Preferred qualifications: Experience with healthcare analytics, particularly perioperative services, inpatient care, emergency department metrics, or operating room utilization. Familiarity with Posit Connect or similar platforms for deploying and managing R/Shiny applications. Experience with AWS data lake architecture or other cloud-based data platforms. Knowledge of Snowflake or other modern cloud data warehouse technologies. Familiarity with Agile or Scrum development methodologies. Experience in consulting or professional services, particularly in environments where client-facing deliverables must align with engagement milestones. Benefits Competitive base salary: Estimated annual base salary ranging from $110,000 to $150,000 , depending on experience, skills, certifications, and other relevant factors. Performance-based compensation: Eligibility for an annual incentive compensation program, with estimated total compensation, including incentive opportunities, ranging from $126,500 to $172,500 . Healthcare coverage: Access to medical, dental, and vision insurance plans, subject to eligibility requirements. Wellness programs: Access to employee wellness initiatives and support programs. Remote work flexibility: The position is listed as remote within the United States, with the role associated with the Chicago location. Professional growth: Opportunities to deepen expertise in healthcare analytics, AI engineering, cloud infrastructure, and full-stack product development. Technical ownership and influence: The opportunity to shape product architecture, influence platform strategy, and contribute to the development of emerging AI capabilities. Collaborative environment: Work alongside engineering professionals, healthcare consultants, and clinical stakeholders on solutions designed to improve healthcare operations and decision-making. Meaningful impact: Help healthcare organizations improve operational efficiency, optimize clinical processes, and support better patient outcomes through data-driven technology.