• Own the reliability, availability, and accuracy of our data infrastructure • Build, maintain, and improve our data pipeline using our modern data stack • Build centralized, durable, and reusable data models • Build and maintain a semantic layer with canonical dimension and metric definitions • Partner closely with Analysts, PMs, Engineers, Marketing, Legal, Fraud, and other stakeholders • Build Reverse ETL pipelines in Python • Proactively bring in new data sources • Champion data governance and help elevate the organization's data maturity

• Design and build full-stack features spanning React frontends, Python/FastAPI backends, and supporting data models and transformations • Develop and maintain data models and transformation pipelines (dbt preferred) that feed application and analytics layers; ensure data flowing into applications is well-modeled, tested, and reliable • Design and implement REST APIs serving clinical data, quality metrics, care gaps, and decision support content to internal applications and external partners • Implement API contracts, versioning strategies, authentication/authorization patterns (OAuth/OIDC), and rate limiting for compliant clinical data access • Build responsive, accessible React user interfaces with modern component patterns; collaborate with product and clinical teams to translate requirements into intuitive UIs • Design and implement comprehensive testing strategies — unit tests, integration tests, end-to-end tests, and data validation tests — to ensure reliability across the stack • Conduct and support QA activities including test planning, test case design, manual testing, and establishing testing standards; work closely with QA engineers and clinical testers to validate functionality and user experience • Write clean, tested, maintainable code across the stack; participate actively in code review and help raise code quality standards • Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, or similar) deliberately and effectively — leveraging them for scaffolding, refactoring, test generation, and documentation while maintaining code quality and understanding • Define and measure success metrics for features — including usage, adoption, clinical workflow impact, and data quality — to drive iterative improvements and prioritization • Partner with product and data teams to establish KPIs and dashboards that measure feature impact on clinician workflows, care coordination, and operational efficiency • Troubleshoot and resolve issues across the full stack — from UI bugs to API failures to data pipeline problems; trace issues end-to-end and implement durable fixes • Collaborate with data engineering to ensure API data contracts are well-defined and upstream data models support application needs • Participate in architecture and design discussions including API design, authentication patterns, data contract definition, and system reliability • Raise data quality or data modeling concerns early in the development process rather than letting them surface downstream • Contribute to technical documentation including API specifications, data dictionaries, runbooks, and architectural decisions • Support production systems through on-call rotations, incident response, and post-incident improvements • Mentor junior engineers and contribute to team process improvement and knowledge sharing

• Handle support tickets and operational issues reported by internal teams and external partners; investigate root causes and coordinate resolution with senior engineers • Perform KTLO (Keep The Lights On) tasks including monitoring pipeline health, responding to alerts, validating data quality, and investigating data anomalies • Conduct data source discovery and profiling work — examining raw data sources, documenting data structure, identifying quality issues, and recommending integration approaches • Assist with data validation and testing — writing SQL queries to validate data transformations, identifying gaps and inconsistencies, and flagging issues for review • Support data quality initiatives by running diagnostics, documenting data quality findings, and escalating issues with clear context for senior engineers • Assist in establishing and monitoring data quality metrics — working with senior engineers to define quality KPIs and track pipeline health • Help maintain and improve documentation for existing data systems, pipelines, and data sources — documenting schemas, transformation logic, and known issues • Assist senior engineers with debugging data pipeline issues — tracing data through transformations, validating intermediate outputs, and comparing expected vs. actual results • Conduct quality assurance activities — reviewing data outputs, testing transformations, and validating correctness before data reaches downstream consumers • Perform exploratory data analysis to understand data patterns, support analytics requests, and help answer business questions about data availability and quality • Learn and apply data engineering best practices including version control (Git), code review processes, and testing frameworks under guidance from senior engineers • Support infrastructure and operational tasks as assigned — assisting with deployments, maintaining environments, and supporting on-call activities • Participate in knowledge-sharing and mentorship; ask questions, document learnings, and contribute to team documentation and runbooks

• Desenvolver, evoluir e sustentar pipelines de dados escaláveis para ingestão, transformação e disponibilização de informações em ambiente Databricks, utilizando Python, SQL, Spark e DBT; • Projetar, implementar e optimizar processos de ETL/ELT, garantindo alta performance, confiabilidade, governança e qualidade dos dados ao longo do ciclo de vida das soluções; • Construir, manter e monitorar workflows e DAGs no Apache Airflow, assegurando a correta orquestração, automação e observabilidade das cargas de dados; • Atuar na modelagem e implementação das camadas Bronze, Silver e Gold seguindo a arquitetura Lakehouse e as melhores práticas de Data Engineering; • Integrar dados provenientes de múltiplas fontes, como APIs, bancos de dados relacionais e não relacionais, sistemas corporativos e serviços em nuvem; • Apoiar a definição de padrões técnicos, boas práticas de desenvolvimento, versionamento, testes e documentação de pipelines de dados; • Trabalhar em parceria com equipes de Analytics, BI, Data Science e áreas de negócio para entender requisitos e transformar necessidades em soluções de dados escaláveis; • Investigar e solucionar incidentes, problemas de performance e falhas em processos de dados, garantindo a estabilidade e disponibilidade da plataforma.