Especialista de Prevencao a Fraude

Accountabilities:: Act as the technical data reference for a large-scale relational intelligence and fraud analytics platform. Lead the evolution of data ingestion and update pipelines, ensuring reliability, scalability, and operational efficiency. Establish and strengthen standards for Data Quality, Data Lineage, Data Governance, Observability, and monitoring. Evolve graph-oriented data models and manage complex entities and relationships at high volume. Support the platform's expansion into new business verticals, products, and analytical use cases. Partner with Engineering, Product, Architecture, Security, and business stakeholders to define technical solutions and priorities. Optimize data pipelines, platform performance, and cloud infrastructure costs within AWS environments. Contribute to the development of analytical capabilities, APIs, and services focused on relational intelligence. Participate in defining the future architecture of the data platform and support integrations with AI models, analytics solutions, and intelligent agents. During the first 12 months, help establish data governance and quality practices, industrialize data pipelines, improve observability, reduce operational data risks, and become a key technical reference for the product. Requirements Solid professional experience in Data Engineering, including experience acting as a technical reference or data technical lead. Proven experience working with high-volume data environments and cloud-based data architectures, preferably AWS. Advanced Python and SQL skills, with hands-on experience building and optimizing data pipelines. Experience with Apache Airflow, Git, REST APIs, ETL/ELT processes, and data orchestration. Strong knowledge of Data Quality, Data Lineage, Data Governance, data modeling, data observability, and pipeline optimization. Advanced AWS knowledge, particularly with services such as S3, IAM, CloudWatch, DynamoDB, Athena, or equivalent analytics services. Understanding of graph theory and complex relationship modeling. Strong analytical and architectural thinking, combined with technical autonomy and the ability to influence technical decisions. Excellent communication skills and the ability to collaborate effectively with both technical and business stakeholders in multidisciplinary environments. Experience with Neo4j, Amazon Neptune, graph databases, graph analytics, relationship intelligence, or entity resolution is highly desirable. Familiarity with fraud analytics, compliance analytics, AML/CTF, identity and fraud, CI/CD for data platforms, Databricks, Spark, OpenSearch, Knowledge Graphs, or GenAI applied to data is a plus. Exposure to MCP and LLM integrations is also considered an advantage. Benefits Opportunity to work with a large-scale relational data ecosystem involving hundreds of millions of entities and complex relationship networks. Exposure to real-world challenges across fraud prevention, digital identity, compliance, risk, investigations, and business intelligence. Work with modern cloud and graph database technologies in a high-volume production environment. Collaboration with multidisciplinary teams across engineering, product, architecture, security, analytics, and business functions. Opportunities to contribute to emerging applications of AI, GenAI, analytics, intelligent agents, and relationship intelligence. Inclusive, people-first, and purpose-driven working environment. Support for accessibility and workplace accommodations for professionals with disabilities or special needs.