Engenheiro de Dados e Plataforma de Agentes Sênior (GCP)

Accountabilities:: Design, develop, and maintain batch and streaming data ingestion, transformation, and quality pipelines using Google Cloud services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage. Build and organize scalable Data Lake and Lakehouse architectures using BigQuery and BigLake, implementing appropriate data layers, partitioning strategies, and performance and cost optimizations. Define and maintain data contracts, including schemas, data freshness requirements, and service-level agreements (SLAs), while monitoring data quality, consistency, and lineage. Prepare and optimize datasets for AI applications through data cleaning, anonymization, chunking, and embedding generation to support Vertex AI RAG Engine, Vertex AI Search, and vector databases. Ensure compliance with Brazil's General Data Protection Law (LGPD) when processing sensitive healthcare information, implementing masking, anonymization, Sensitive Data Protection, policy tags, and data residency controls before information reaches AI environments. Design secure cross-project data access architectures using Cloud IAM, Dataplex, BigLake, and Analytics Hub, minimizing unnecessary data duplication while enforcing least-privilege access and auditability. Enable internal systems and data sources to be consumed as tools by AI agents through MCP servers and MCP Toolbox, collaborating with AI teams on integrations with Vertex AI Agent Engine and Agent Development Kit (ADK). Develop and maintain MLOps and LLMOps pipelines using Vertex AI Pipelines, incorporating model and response evaluations, security guardrails through Model Armor, and monitoring for data drift and response quality. Provision and manage data and AI infrastructure as code using Terraform and CI/CD practices, ensuring consistent, automated, and scalable deployments. Implement FinOps practices by monitoring and allocating infrastructure, project, and model costs through BigQuery billing exports and resource labels. Establish end-to-end observability using OpenTelemetry, Cloud Logging, Cloud Monitoring, and Cloud Trace to monitor system health, performance, and reliability. Contribute to resilience and disaster recovery strategies, ensuring the availability and continuity of critical data and AI services. Collaborate with data scientists, AI engineering teams, business stakeholders, and external partners to communicate technical decisions, document architectures, and establish clear ownership of pipelines, data sources, and tools. Requirements: Strong proficiency in Python and SQL, with the ability to develop efficient, scalable, and maintainable data engineering solutions. Solid hands-on experience with Google Cloud Platform (GCP) data engineering services, particularly BigQuery, Cloud Storage, Dataflow or Dataproc, and Pub/Sub. Experience designing, developing, and orchestrating data pipelines using Cloud Composer/Airflow, Cloud Workflows, or similar orchestration tools. Knowledge of GCP data governance and security services, including Cloud IAM, Dataplex, policy tags, and Cloud KMS. Practical experience with Terraform and CI/CD tools such as Cloud Build, GitHub Actions, or equivalent platforms. Experience working with GCP environments involving multiple projects, organizations, and resource folders. Strong understanding of distributed and event-driven architectures, including messaging systems, retries, idempotency, and data consistency. Practical experience preparing and managing data for at least one production-level LLM application, such as Retrieval-Augmented Generation (RAG) or AI agents with tool-calling capabilities. Knowledge of LGPD requirements for sensitive data processing, including data anonymization, masking, and privacy protection techniques. Strong communication skills, with the ability to collaborate effectively with data scientists, AI engineers, business teams, and external stakeholders. High standards of security, responsibility, and ethical judgment when handling sensitive information, particularly patient data accessed by autonomous AI agents. Ability to evaluate and communicate technical trade-offs involving data duplication versus cross-project access, batch versus streaming architectures, and infrastructure costs versus latency. Strong documentation habits and the ability to establish clear ownership and responsibilities across technical teams. Nice to have: Experience with Vertex AI services, including RAG Engine, Vertex AI Search, Agent Engine, and Vertex AI Pipelines. Familiarity with Gemini and Model Armor. Experience with agent development frameworks such as ADK, LangChain, LangGraph, or CrewAI. Practical experience developing and implementing MCP servers. Knowledge of vector database technologies, including Vertex AI Vector Search, AlloyDB with pgvector, and BigQuery Vector Search. GraphRAG experience is an additional advantage. Previous experience in healthcare technology or working with clinical data standards such as HL7, FHIR, and Cloud Healthcare API. Knowledge of Data Mesh architectures, hub-and-spoke models, and FinOps practices within GCP environments. Relevant professional certifications, such as Google Cloud Professional Data Engineer, Google Cloud Professional Machine Learning Engineer, or FinOps Certified Practitioner. Benefits: Health and Well-being Porto Seguro medical insurance, with the possibility of including your spouse and children. Porto Seguro dental insurance for employees and eligible dependents. Total Pass membership, offering discounted gym and fitness plans for employees and their families. Mindself wellness program, promoting mindfulness, meditation, and improved quality of life. Financial Benefits and Allowances Profit Sharing Program (PLR), recognizing employee contributions to business performance. Alelo meal and food allowances. Home office allowance to support a comfortable and productive remote workspace. Livelo reward points, offering flexibility in how you use your accumulated rewards. Childcare allowance to support working parents. Professional Development Partnerships with educational institutions, providing discounts and incentives for courses and degree programs. Financial support and incentives for professional certifications, including GCP, Azure, AWS, and other cloud technologies. Opportunities for continuous learning and professional development in data engineering, cloud computing, and artificial intelligence. Work Environment and Flexibility Fully remote working arrangement. Collaborative environment that values teamwork, innovation, ethics, and transparency. Opportunities to work with modern technologies and challenging data and AI projects.