• Design, build, and own scalable data platforms that power analytics, machine learning, regulatory reporting, and mission-critical operational systems. • Develop robust ELT workflows, data models, and distributed processing jobs using modern cloud data technologies. • Implement modern data engineering solutions using technologies such as Python, SQL, dbt, Airflow, Snowflake, Tableau, Power BI, AWS data services, containerization (Docker/Kubernetes), and related cloud-native tools. • Leverage best practices to ensure data quality, integrity, lineage, governance, performance, and reliability across all pipelines, data platforms, and analytics applications. • Establish and maintain SLAs, monitoring, and alerting for mission-critical data services with observability design and implementation. • Identify and resolve performance bottlenecks to ensure scalable and cost-efficient data processing.

• Define and maintain enterprise data architecture standards across structured, semi-structured, and unstructured data domains — with deliberate design for AI/ML workloads, including feature stores, vector databases, and embedding pipelines. • Use AI-assisted modeling tools to accelerate data model design, evaluate architectural trade-offs, and validate designs against business requirements before committing to implementation. • Design and govern the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS — ensuring they are optimized for both analytical and AI/ML consumption patterns. • Establish data ontology, taxonomy, and semantic layer standards that enable AI systems to reason over organizational data accurately and consistently. • Evaluate emerging data architecture patterns — including retrieval-augmented generation (RAG), real-time feature serving, and vector search — and build a roadmap for their adoption across Stellus Rx. • Design scalable data models and ELT/ETL pipeline architectures that support both traditional analytics and AI/ML model training and inference workloads. • Use AI code generation tools to accelerate the authoring and validation of data models, transformation logic, and pipeline configurations — replacing manual, repetitive design work with intelligent, AI-assisted development. • Define standards for data partitioning, indexing, caching, and storage optimization; use AI-driven performance analysis to continuously validate and improve architectural decisions. • Partner with Data Engineers to translate architectural blueprints into production-ready pipelines, providing hands-on guidance and AI-augmented design reviews. • Define and enforce data governance frameworks, data quality standards, and data contracts across the enterprise — using AI-powered data observability tools to automate quality monitoring and surface issues proactively rather than through manual review. • Ensure data architecture meets compliance requirements relevant to healthcare (HIPAA, SOC 2, NIST); use AI-assisted compliance tooling to continuously monitor for policy drift and streamline audit evidence generation. • Develop and maintain a master data management (MDM) strategy that ensures consistency, accuracy, and trustworthiness of critical data assets across systems. • Champion data privacy and security principles in architectural design, including data lineage tracking, access controls, and anonymization strategies for sensitive healthcare data. • Design data infrastructure that serves as the foundation for AI/ML initiatives — ensuring data is accessible, well-labeled, versioned, and structured to support model training, validation, and ongoing inference at scale. • Collaborate with data scientists and ML engineers to understand modeling requirements and translate them into data architecture decisions that reduce friction in the AI development lifecycle. • Use AI-assisted analysis to identify high-value data assets that are underutilized, and develop strategies to unlock their potential for analytics and AI-driven decision-making. • Partner with Business Intelligence and Product teams to ensure the data architecture supports self-service analytics, real-time dashboards, and AI-powered reporting capabilities. • Define and maintain data architecture standards, patterns, and best practices across the organization; use AI tools to generate, review, and keep documentation current with minimal manual overhead. • Mentor Data Engineers and Analysts, guiding them in applying architectural standards and AI-augmented data development practices. • Communicate architectural decisions, trade-offs, and roadmap recommendations clearly to both technical teams and executive leadership. • Stay current on emerging data technologies, AI/ML data infrastructure trends, and industry best practices; provide recommendations on adoption timing and implementation approach.

• Design and maintain data ingestion and transformation pipelines. • Integrate multiple data sources in various formats. • Ensure data quality, integrity, and reliability. • Optimize performance for high-volume data extraction processes. • Collaborate with technical teams using event-driven architectures.

• Projetar e desenvolver pipelines escaláveis e robustos de ingestão e transformação de dados em AWS; • Construir e sustentar arquitetura de dados moderna com organização em camadas para consumo analítico; • Integrar ambientes transacionais e diversas fontes corporativas à arquitetura de dados em nuvem; • Aplicar modelagem multidimensional Star Schema / Snowflake para suporte a Analytics e BI; • Garantir performance e escalabilidade por meio de particionamento estratégico, paralelismo e otimização de processamento; • Desenvolver e otimizar rotinas de transformação e processamento de dados com foco em eficiência e confiabilidade; • Automatizar processos com foco em reusabilidade, monitoramento e qualidade; • Atuar com governança, rastreabilidade e requisitos de segurança, auditoria e compliance; • Apoiar a evolução da arquitetura de dados em nuvem, garantindo boas práticas de engenharia e sustentação operacional.