• Especificar e evoluir a arquitetura de dados da empresa, garantindo sua escalabilidade e eficiência; • Definir e aplicar melhores práticas de governança de dados; • Traduzir demanda de negócio para uma arquitetura técnica; • Orientar equipes de negócio e técnicas sobre processos para implementar/modificar uma arquitetura; • Colaborar com equipes multidisciplinares para garantir a integração e a qualidade dos dados; • Promover a adoção de uma cultura de data mesh na organização; • Definir guide de melhores práticas e arquitetura de dados.

Role Description This role sits at the intersection of artificial intelligence and robust enterprise integration. Define the technical blueprint for scaling AI adoption across the organization, focusing specifically on LLMs, RAG, and predictive analytics, while building the high-performance API and messaging backbones required to fuel these real-time systems. - AI Strategy & Architecture: Define the technical blueprint and roadmap for enterprise AI adoption. Architect scalable frameworks for LLM integration, RAG, and predictive modeling, ensuring cost-efficiency, scalability, and ethical AI standards. - Data Integration & APIs: Design high-performance, secure API services (RESTful, GraphQL) and event-driven architectures to connect disparate systems and power real-time AI applications. - AI & Data Governance: Establish guardrails for data privacy, compliance, and security regarding AI training data, model inputs, and API access management. - Cross-Functional Leadership: Collaborate with executive leadership, data scientists, and product teams to translate business goals into production-ready AI and integration strategies. Qualifications - 8+ years of experience in software architecture, system integration, or machine learning engineering, with at least 2-3 years dedicated to productionizing AI/LLM solutions. - Preferred experience in Azure Databricks. Benefits - Significant career development opportunities exist as the company grows. - The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

• Build data pipelines to create data foundation for Process Mining tools to identify bottlenecks, inefficiencies, conformance deviations, and potentials for process standardization, improvement, and automation in cooperation with process owners and process experts • Understand business needs around data integration, data engineering, and data operations to build end-to-end data pipelines for analytical purposes • Manage and troubleshoot data pipelines in Azure, Databricks and Datasphere environments • Work together with Process Mining experts to support analysis of processes

Role Description As a Data Software Engineer, you will be responsible for building new data solutions for our rapidly expanding customer base and working with the top data ingestion technologies, working with a team of amazing, diverse-minded, and bright people who make an impact, generate creative & innovative ideas, and take on new perspectives. Responsibilities - Own end-to-end data flows from requirements and architecture through implementation and production operations, including Data acquisition, Data set acceptance criteria, and Data Science integration. - Design and build scalable batch and real-time data pipelines and lakehouse solutions with a focus on large-scale data processing. - Take responsibility to explore technologies to scale up the Data ecosystem to handle rapid Big Data growth. - Partner with Data Science to productionize ML/AI workloads and ensure smooth integration into products. - Collaborate with cloud, DevOps, application, and client teams to deliver robust, secure, and scalable solutions that solve meaningful business problems. - Evaluate and adopt new technologies and patterns to evolve the data ecosystem as scale and complexity grow. Qualifications - 3+ years of hands-on Data Engineering building and operating production-grade Data Systems and Pipelines (Data-Intensive, Distributed Processing, Databases). - B.Sc. / M.Sc. in Computer Science, Computer Engineering, or equivalent. - Proficiency in Python; working proficiency in Scala. - Strong expertise with at least one major cloud provider (AWS, Azure, or GCP). - Strong experience with Big Data processing (Spark, DataBricks) and event streaming (Kafka). - Experience with orchestration and platform tooling such as Airflow; ability to build maintainable DAGs and operationalize workflows. - Strong SQL skills and experience with data storage systems plus at least one of: Data Lake/Lakehouse, columnar DB, or NoSQL systems. - Hands-on experience with containers and Kubernetes (Helm is a plus) and modern CI/CD practices. - Familiarity with LLM workflow frameworks (LangChain/LangGraph). - Proven experience designing, building, and owning production-grade data pipelines (batch and/or streaming), including reliability, backfills, and SLA-driven delivery. - Ability to learn new technologies and work in a dynamic fast-paced environment. - Result-driven, pragmatic, and innovative. - Strong analytical skills, with an open and proactive mindset to investigate, learn, and propose solutions; highly self-driven and self-taught. - Strong English communication skills, both written and verbal. Requirements - Experience with Delta Lake and/or Apache Iceberg; ML lifecycle tools such as MLflow. - Experience with Pandas/Polars and building data services/APIs (e.g., FastAPI). - Experience building LLM-powered agents / chat assistants (RAG, tool/function calling, workflow or multi-agent orchestration), using modern frameworks and platforms. - Infrastructure as Code (Terraform/Pulumi/CloudFormation) and cloud security fundamentals (IAM, secrets, encryption). - Experience with observability tooling (metrics/logging/tracing) and cost/performance optimization for distributed workloads. - Experience building applications with React and Node.js. Benefits - Flexible working environment - Volunteer time off - LinkedIn Learning - Employee-Assistance-Program (EAP)