[Job-32170] Senior Data Engineer Brazil

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Na CI T , ajudamos grandes empresas a transformar o potencial da AI em impacto real nos negócios com AI Deployment, execução AI-native e tech-integrated business solutions. Com 30 anos de experiência em transformação tecnológica, aceleramos inovação com expertise em agentic SDLC, application modernization, Data AI, martech e business strategy. Somos 8.000 CI Ters em mais de 25 países, colaborando para construir soluções com impacto real. AI já faz parte da forma como trabalhamos, evoluímos e inovamos todos os dias. Importante : se você reside na Região Metropolitana de Campinas, sua presença nos escritórios da cidade será obrigatória, conforme a política de frequencia vigente. We are seeking a talented and experienced Senior Data Engineer to join our team and support a strategic partner in the evolution, maintenance, and continuous improvement of global financial closing processes. This role will focus on supporting financial data reconciliation, P L routines, existing data pipelines, and the integration of new data sources. The professional will also contribute to building new pipelines, improving data quality and support initiatives involving AI models and advanced data capabilities. We are looking for someone hands-on, collaborative, and analytical, who enjoys solving complex data challenges and building reliable, scalable solutions that create real business impact. Responsibilities Data Pipeline Development: Develop, maintain, and improve data pipelines using Databricks, PySpark, Python, and SQL to transform raw financial data into reliable curated datasets. Data Integration: Integrate new data sources into the existing data ecosystem, helping expand the platform’s capabilities and improve business visibility. Financial Data Reconciliation: Support financial reconciliation activities, P L routines, and global closing processes by ensuring data consistency, accuracy, and traceability across different sources. Data Processing and Optimization: Process large volumes of structured and semi-structured data, optimizing transformations, storage formats, and query performance using Delta Lake and Lakehouse best practices. Pipeline Maintenance and Support: Monitor, troubleshoot, and support existing pipelines, identifying issues, investigating discrepancies, and ensuring reliable daily and monthly processing. Continuous Improvement: Propose and implement better technical approaches to improve performance, scalability, maintainability, and data quality across the financial data ecosystem. Collaboration with Cross-functional Teams: Work closely with existing data engineers, business stakeholders, finance teams, and technology teams to understand needs and deliver high-quality data solutions. Governance and Data Quality: Support data governance, validation rules, auditability, documentation, and controls to ensure trustworthy and well-managed financial data. AI Integration: Support initiatives involving AI models and advanced data capabilities, helping integrate AI-driven insights and automation into data workflows. Requirements Strong experience with Databricks, including notebooks, jobs, workflows, clusters, and best practices for building scalable data solutions. Proficiency in data processing using PySpark, Python, and SQL, with hands-on experience transforming raw data into curated and reliable datasets. Experience with Delta Lake and Lakehouse architecture, including Delta tables, incremental processing, data optimization, and structured data layers. Knowledge of Azure data ecosystem, especially Azure Data Lake Storage, data integration patterns, and cloud-based data processing. Experience supporting financial data pipelines, preferably involving reconciliation, P L, financial closing, or other critical business processes. Hands-on experience maintaining and improving existing pipelines, including troubleshooting, performance tuning, documentation, and production support. Ability to work with structured and semi-structured data,

在CI T，我们帮助大型企业将AI潜力转化为真正的业务影响，通过AI Deployment、AI-native执行和技术集成的业务解决方案。凭借30年的技术转型经验，我们以agentic SDLC、application modernization、Data AI、martech和business strategy方面的专业知识加速创新。我们在超过25个国家拥有8，000名CI Ters，共同构建具有真正影响力的解决方案。AI已经成为我们每天工作、进化和创新方式的一部分。重要提示：如果您居住在Campinas大都会区，根据现行出勤政策，您必须到该市办公室现场办公。我们正在寻找一位才华横溢且经验丰富的高级数据工程师加入我们的团队，支持一个战略合作伙伴在全球财务结账流程的演进、维护和持续改进方面的工作。该职位将专注于支持财务数据对账、P L例程、现有数据管道以及新数据源的集成。该专业人员还将参与构建新管道、提升数据质量，并支持涉及AI模型和高级数据能力的相关举措。我们正在寻找一位动手能力强、善于协作且具备分析能力的人，喜欢解决复杂的数据挑战，并构建可靠、可扩展的解决方案以创造真正的业务影响。职责 数据管道开发：使用Databricks、PySpark、Python和SQL开发、维护和改进数据管道，将原始财务数据转化为可靠的精选数据集。数据集成：将新数据源集成到现有数据生态系统中，帮助扩展平台能力并提升业务可见性。财务数据对账：通过确保不同来源之间的数据一致性、准确性和可追溯性，支持财务对账活动、P L例程和全球结账流程。数据处理与优化：处理大量结构化和半结构化数据，使用Delta Lake和Lakehouse最佳实践优化转换、存储格式和查询性能。管道维护与支持：监控、排查并支持现有管道，识别问题、调查差异，并确保可靠的每日和每月处理。持续改进：提出并实施更好的技术方案，以提升财务数据生态系统的性能、可扩展性、可维护性和数据质量。与跨职能团队协作：与现有数据工程师、业务利益相关者、财务团队和技术团队紧密合作，理解需求并交付高质量的数据解决方案。治理与数据质量：支持数据治理、验证规则、可审计性、文档和控制，以确保财务数据可信且得到良好管理。AI集成：支持涉及AI模型和高级数据能力的相关举措，帮助将AI驱动的洞察和自动化集成到数据工作流中。要求 具备丰富的Databricks经验，包括notebooks、jobs、workflows、clusters，以及构建可扩展数据解决方案的最佳实践。熟练使用PySpark、Python和SQL进行数据处理，并具备将原始数据转化为精选且可靠数据集的实操经验。具备Delta Lake和Lakehouse架构经验，包括Delta tables、增量处理、数据优化和结构化数据分层。了解Azure数据生态系统，尤其是Azure Data Lake Storage、数据集成模式和基于云的数据处理。具备支持财务数据管道的经验，最好涉及对账、P L、财务结账或其他关键业务流程。具备维护和改进现有管道的实操经验，包括故障排查、性能调优、文档编写和生产支持。能够处理结构化和半结构化数据，

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