Role Description Buscamos um(a) profissional para atuar como referência técnica em Engenharia de Dados, liderando a evolução da plataforma de dados e garantindo a entrega de soluções escaláveis, eficientes e alinhadas às necessidades do negócio. - Atuar como referência técnica na arquitetura de dados, com foco em ambientes Databricks e AWS. - Ser responsável pelos pipelines críticos de dados, incluindo ingestão, processamento e disponibilização (serving). - Garantir a adoção de padrões de engenharia, assegurando qualidade de código, testes, observabilidade, governança e cumprimento de SLAs. - Liderar decisões de arquitetura e design técnico, considerando aspectos de escalabilidade, performance, segurança e otimização de custos. - Atuar ativamente em iniciativas de FinOps, promovendo a eficiência operacional e a melhor utilização dos recursos da plataforma. - Orientar e desenvolver engenheiros de dados por meio de mentoria, code reviews e disseminação de boas práticas. - Trabalhar em parceria com stakeholders de negócio, produto e tecnologia para traduzir demandas em soluções robustas e escaláveis. - Apoiar a priorização técnica do roadmap, equilibrando necessidades de entrega, sustentabilidade da plataforma e redução de dívida técnica. - Liderar a gestão e resolução de incidentes críticos, contribuindo para a evolução da maturidade operacional do ambiente de dados. - Promover a melhoria contínua dos processos, ferramentas e práticas da área de Engenharia de Dados. Qualifications - Experiência consolidada em engenharia de dados (+5 anos) - Experiência prévia em papel de liderança técnica ou como referência dentro do time - Forte domínio de SQL, Python e/ou Scala - Experiência com arquiteturas modernas de dados (Data Lake / Lakehouse) - Experiência prática com Spark / Databricks (ou similar) - Experiência com cloud (preferencialmente AWS) - Conhecimento sólido em modelagem de dados (analítica e operacional) - Experiência com CI/CD, versionamento e boas práticas de engenharia Requirements - Experiência com governança em escala (ex: Databricks Unity Catalog) - Implementação de Data as a Product / Data Mesh / Embedded Data - Experiência com streaming (Kafka, Kinesis) - Data Observability e Data Quality frameworks - Experiência com FinOps aplicado a dados - Uso de IA para aumento de produtividade em engenharia de dados Soft Skills - Capacidade de influenciar sem autoridade formal - Comunicação clara com áreas técnicas e de negócio - Tomada de decisão baseada em trade-offs (custo, prazo, qualidade) - Capacidade de estruturar problemas complexos - Mentalidade de dono (ownership) sobre a plataforma Benefits - A Serasa Experian é muito mais do que você imagina. Com o propósito de criar um futuro melhor, ampliando oportunidades para pessoas e empresas. - Compromisso em construir uma cultura inclusiva e um ambiente equilibrado entre carreira e interesses pessoais. - Reconhecimento como uma das melhores e mais inovadoras empresas para se trabalhar no país. - Premiações pelo Great Place To Work™ em 24 países e certificação internacional Top Employers. - Avaliação de 4,6 no Glassdoor.

Role Description We are looking for an experienced Data Engineer to join a growing data and analytics team focused on building modern, cloud-based data solutions. This role will be responsible for designing, developing, and optimizing scalable data platforms that support enterprise reporting, analytics, and data-driven decision making. The ideal candidate has hands-on experience with Microsoft's Azure Data Platform and a strong understanding of data engineering best practices, including data governance, data quality, and data management. Key Responsibilities - Design, develop, and maintain scalable data pipelines and ETL/ELT processes. - Build and optimize data solutions using Azure cloud technologies. - Develop data ingestion, transformation, and integration processes from multiple data sources. - Create and maintain data models that support reporting, analytics, and business intelligence initiatives. - Develop reusable and high-performance data workflows using SQL and Python. - Collaborate with Data Analysts, Data Scientists, Software Engineers, and business stakeholders to translate requirements into technical solutions. - Enhance data architecture through automation, optimization, and performance improvements. - Support CI/CD processes and deployment pipelines for data engineering solutions. - Build and maintain datasets that support enterprise reporting and analytics platforms. - Troubleshoot production data issues and perform root cause analysis. - Participate in code reviews and contribute to engineering best practices. Qualifications - Strong experience with SQL development. - Experience building and maintaining enterprise-scale data pipelines. - Hands-on experience with Microsoft Azure (MUST). - Experience with Azure Synapse Analytics. - Experience with Azure Databricks. - Experience with Azure Data Factory. - Experience with Azure Data Lake. - Strong Python programming skills. - Experience with source control and CI/CD methodologies. - Experience working with large datasets and distributed data processing. - Understanding of data modeling and modern data architecture principles. - Experience with data integration and transformation processes. - Strong analytical and problem-solving skills. - Excellent communication and collaboration abilities. - Experience with Azure cloud services and data platform technologies. - Experience with Azure Kubernetes Service (AKS). - Experience with Kubernetes. - Experience using Ansible for infrastructure automation and configuration management. - Experience creating and maintaining technical documentation using Markdown. - Experience with Azure DevOps (ADO) for source control, work item management, and CI/CD pipelines. - Experience with GitHub and Git-based development workflows. - Experience with Microsoft Power Platform (Power BI, Power Apps, Power Automate, and Power Pages). - Experience with Ontology Engineering and semantic data modeling. - Experience writing queries using SPARQL. - Experience writing queries using Cypher. - Experience developing and consuming GraphQL APIs. - Experience designing and working with graph databases and knowledge graph solutions. Preferred Qualifications - Experience with Power BI datasets and reporting solutions. - Experience with data governance frameworks and data quality initiatives. - Understanding of enterprise data management practices. - Exposure to Big Data technologies. - Experience working within Agile environments. - Ability to collaborate effectively with both technical and business teams.

• Lead the design and evolution of the data ecosystem supporting advanced data forensics, fraud detection, investigative analytics, and threat intelligence • Establish the architecture, governance, and technical foundations necessary to deliver scalable analytics, machine learning, and investigative intelligence capabilities • Design and maintain enterprise-scale data architectures supporting forensic analytics and security operations • Develop target-state architectures that support both operational reporting and advanced analytical workloads • Architect and optimize cloud-based analytics platforms utilizing Azure and Databricks technologies • Implement enterprise data governance standards and controls • Build and maintain architectures that support machine learning, artificial intelligence, and advanced analytics workloads.

• Build advanced data pipelines utilizing the Medallion Architecture to create high-quality data sources in Snowflake. • Leverage AI-assisted development tools to reduce manual pipeline work and explore emerging Snowflake capabilities like Cortex to support AI/ML-enabled workflows. • Build Data Applications that contribute to interactive experiences, including Streamlit-based apps, transforming Snowflake from a warehouse into a full-service data platform. • Create robust data pipelines using AWS technologies to serve specific business needs. • Design and evolve analytical data models that support BI, advanced analytics, and AI workloads. • Partner directly with Analytics and Data Science teams to translate complex business questions into well-structured, modular data assets. • Write complex Python/SQL, scripts, and stored procedures to ensure 99.95% uptime and optimize query performance for downstream consumption. • Serve as a primary advisor to the Data Management team to identify opportunities for technical improvements and automation. • Act as a leader for junior and mid-level engineers, providing direction on programming best practices, query optimization, and business tact.