• Design, build, and optimize complex Power BI semantic models, advanced DAX measures, and executive-grade dashboards • Develop and tune dbt models and SQL transformations against Snowflake and other modern cloud data platforms • Own end-to-end data pipelines that consolidate CRM, ERP, product, and financial data into trusted, reusable layers • Diagnose and resolve performance, accuracy, and usability issues in existing reporting independently • Establish and enforce semantic modeling patterns, KPI definitions, naming conventions, and code review practices • Mentor mid-level and junior analytics team members through pairing, review, and documentation • Partner with the Director to shape the analytics architecture and reduce technical debt over time • Champion governance and data quality controls without slowing the team down • Work directly with RevOps and FP&A stakeholders to define metrics, forecasting views, and performance frameworks • Translate evolving requirements into intuitive, high-quality visualizations leaders trust and use • Present findings clearly to technical and non-technical audiences alike

Role Description The AI & Analytics Engineer I supports the design, build, and delivery of user-facing AI-powered applications, data pipelines, and analytics solutions that drive operational efficiency and decision-making across the organization. This is a developing-professional role focused on hands-on execution under the guidance of senior engineers. Core Responsibilities - AI Applications Development - Assist in building and enhancing AI-powered applications and agents that support business workflows. - Build user-facing interfaces using a modern frontend framework (React, Vue, Angular, or similar). - Develop backend services and REST or GraphQL APIs (Python, Node.js, .NET, or similar). - Develop components of AI solutions that automate routine tasks and surface insights. - Gather requirements from stakeholders with guidance from senior team members. - Iterate on AI applications based on user feedback and testing results. - Provide ongoing Level 3 support for software products. - AI Integration & Delivery - Support integration of AI applications with enterprise systems under senior direction. - Assist with deployment, testing, and monitoring of AI solutions in lower and production environments. - Translate documented business requirements into functional workflows. - Follow established standards for reliability, security, and code quality. - Data Engineering & Pipeline Development - Build and maintain ETL/ELT pipelines that feed analytics and AI use cases. - Ingest and transform data from multiple source systems into centralized platforms. - Validate accuracy, completeness, and structure of pipeline outputs. - Analytics & Data Modeling - Develop and maintain semantic models, datasets, and dashboards (Power BI and related tools). - Apply standardized business metrics and KPI definitions across reports. - Optimize queries and data structures for performance and usability. - Implement and maintain row-level security and access controls on reports. - Cross-Functional Collaboration - Partner with IT, clinical informatics, operations, and business stakeholders to understand reporting and AI needs. - Communicate progress, blockers, and trade-offs clearly to both technical and non-technical audiences. - Escalate architectural or scope questions to senior engineers. - Engineering Standards & Quality - Follow team practices for source control, code review, documentation, and testing. - Monitor AI outputs and data pipelines for accuracy and reliability. - Support compliance with data security, privacy, and governance standards (HIPAA-aware). - Continuous Learning & Improvement - Build technical depth in AI/ML tooling, cloud services, and modern data platforms. - Contribute small improvements to existing AI tools, dashboards, and pipelines. - Stay current with emerging AI and analytics technologies relevant to healthcare operations. Scope & Impact - Contributes directly to AI application and analytics delivery used across business operations. - Expands team throughput on AI applications, dashboards, and insight distribution. - Operates under the technical direction of the AI & Analytics Engineer II and Senior Director of AI, Data, & Enterprise Applications. Success Metrics - Volume and quality of analytics and AI deliverables completed. - Reliability and performance of owned pipelines and reports. - Reduction in backlog for analytics and AI requests. - Growth in technical proficiency over the first 12–18 months. - Stakeholder satisfaction with delivered solutions. Qualifications - 2–4 years of experience in data analytics, BI development, data engineering, or software development. - Hands-on experience building custom software products, including writing code and delivering end-to-end solutions aligned to business requirements. - Proven ability to conduct testing, troubleshoot and debug issues, and ensure high-quality, reliable application performance across environments. - Hands-on experience building dashboards, reports, or data pipelines in a professional setting. - Hands-on experience building and shipping user-facing applications end-to-end, including both frontend and backend components. - Demonstrated proficiency with a modern frontend framework (React, Vue, Angular, or similar). - Demonstrated proficiency with a backend framework (FastAPI, Django, Express, ASP.NET Core, Spring Boot, or similar). - Active GitHub profile or equivalent code portfolio with reviewable code samples (required at application). - Exposure to AI, machine learning, or workflow automation projects (academic or professional) preferred. Requirements - Demonstrated proficiency in modern software engineering tools and practices, including Agile development, CI/CD workflows, automated testing, and code review standards. - Daily experience with Git and GitHub, including branching, pull requests, and code review workflows. - Working proficiency with SQL and relational data modeling. - Experience with Power BI (or comparable BI platform) including DAX and semantic models. - Familiarity with ETL/ELT concepts and at least one data integration tool. - Exposure to cloud platforms (Azure preferred) and AI/ML services a plus. - Comfort working with both structured and unstructured data. Competencies - Strong analytical and problem-solving skills with attention to detail. - Willingness to learn from senior engineers and apply feedback quickly. - Effective written and verbal communication with technical and non-technical partners. - Ability to manage multiple concurrent tasks and meet committed delivery dates. - Healthcare or pediatrics domain interest is a plus. Compensation The salary/rate range listed here has been provided to comply with local regulations and represents a potential base salary/rate for this role. Please note that actual salaries/rates may vary within this range above or below, depending on experience and location. EEO Statement PM Pediatric Care is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status or any other characteristic protected by law.

Role Description The AI & Analytics Engineer I supports the design, build, and delivery of user-facing AI-powered applications, data pipelines, and analytics solutions that drive operational efficiency and decision-making across the organization. This is a developing-professional role focused on hands-on execution under the guidance of senior engineers. Core Responsibilities - AI Applications Development - Assist in building and enhancing AI-powered applications and agents that support business workflows. - Build user-facing interfaces using a modern frontend framework (React, Vue, Angular, or similar). - Develop backend services and REST or GraphQL APIs (Python, Node.js, .NET, or similar). - Develop components of AI solutions that automate routine tasks and surface insights. - Gather requirements from stakeholders with guidance from senior team members. - Iterate on AI applications based on user feedback and testing results. - Provide ongoing Level 3 support for software products. - AI Integration & Delivery - Support integration of AI applications with enterprise systems under senior direction. - Assist with deployment, testing, and monitoring of AI solutions in lower and production environments. - Translate documented business requirements into functional workflows. - Follow established standards for reliability, security, and code quality. - Data Engineering & Pipeline Development - Build and maintain ETL/ELT pipelines that feed analytics and AI use cases. - Ingest and transform data from multiple source systems into centralized platforms. - Validate accuracy, completeness, and structure of pipeline outputs. - Analytics & Data Modeling - Develop and maintain semantic models, datasets, and dashboards (Power BI and related tools). - Apply standardized business metrics and KPI definitions across reports. - Optimize queries and data structures for performance and usability. - Implement and maintain row-level security and access controls on reports. - Cross-Functional Collaboration - Partner with IT, clinical informatics, operations, and business stakeholders to understand reporting and AI needs. - Communicate progress, blockers, and trade-offs clearly to both technical and non-technical audiences. - Escalate architectural or scope questions to senior engineers. - Engineering Standards & Quality - Follow team practices for source control, code review, documentation, and testing. - Monitor AI outputs and data pipelines for accuracy and reliability. - Support compliance with data security, privacy, and governance standards (HIPAA-aware). - Continuous Learning & Improvement - Build technical depth in AI/ML tooling, cloud services, and modern data platforms. - Contribute small improvements to existing AI tools, dashboards, and pipelines. - Stay current with emerging AI and analytics technologies relevant to healthcare operations. Scope & Impact - Contributes directly to AI application and analytics delivery used across business operations. - Expands team throughput on AI applications, dashboards, and insight distribution. - Operates under the technical direction of the AI & Analytics Engineer II and Senior Director of AI, Data, & Enterprise Applications. Success Metrics - Volume and quality of analytics and AI deliverables completed. - Reliability and performance of owned pipelines and reports. - Reduction in backlog for analytics and AI requests. - Growth in technical proficiency over the first 12–18 months. - Stakeholder satisfaction with delivered solutions. Qualifications - 2–4 years of experience in data analytics, BI development, data engineering, or software development. - Hands-on experience building custom software products, including writing code and delivering end-to-end solutions aligned to business requirements. - Proven ability to conduct testing, troubleshoot and debug issues, and ensure high-quality, reliable application performance across environments. - Hands-on experience building dashboards, reports, or data pipelines in a professional setting. - Hands-on experience building and shipping user-facing applications end-to-end, including both frontend and backend components. - Demonstrated proficiency with a modern frontend framework (React, Vue, Angular, or similar). - Demonstrated proficiency with a backend framework (FastAPI, Django, Express, ASP.NET Core, Spring Boot, or similar). - Active GitHub profile or equivalent code portfolio with reviewable code samples (required at application). - Exposure to AI, machine learning, or workflow automation projects (academic or professional) preferred. Requirements - Demonstrated proficiency in modern software engineering tools and practices, including Agile development, CI/CD workflows, automated testing, and code review standards. - Daily experience with Git and GitHub, including branching, pull requests, and code review workflows. - Working proficiency with SQL and relational data modeling. - Experience with Power BI (or comparable BI platform) including DAX and semantic models. - Familiarity with ETL/ELT concepts and at least one data integration tool. - Exposure to cloud platforms (Azure preferred) and AI/ML services a plus. - Comfort working with both structured and unstructured data. Competencies - Strong analytical and problem-solving skills with attention to detail. - Willingness to learn from senior engineers and apply feedback quickly. - Effective written and verbal communication with technical and non-technical partners. - Ability to manage multiple concurrent tasks and meet committed delivery dates. - Healthcare or pediatrics domain interest is a plus. Compensation The salary/rate range listed here has been provided to comply with local regulations and represents a potential base salary/rate for this role. Please note that actual salaries/rates may vary within this range above or below, depending on experience and location. We look at compensation for each individual based on experience and qualifications. EEO Statement PM Pediatric Care is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status or any other characteristic protected by law.

• Build and optimize data models using dbt/SQL, following development best practices and governance. • Ensure data governance, quality, documentation, scalability, and business impact. • Participate in discussions with business teams to understand rules and requirements. • Work closely with software engineers to understand data structure, refresh frequency, limitations, business context, and technical specifics of data sources. • Implement data quality tests. • Monitor and maintain pipelines to ensure availability, performance, and integrity of data within your business domain. • Translate business needs into solutions aligned with data platform tools and strategies. • Provide support to the data team regarding the data platform and best practices. • Contribute to the development of Asaas's data platform.