Role Description We at Coforge are hiring a Senior Data Engineer with the following skill set. - Design and implement scalable data pipelines using Databricks and Microsoft Fabric. - Build and maintain a Medallion Architecture (Bronze, Silver, Gold layers) to organize and process data across the organization. - Collaborate with the AI team to ensure high-quality data availability for RAG patterns and LLM components. - Develop and optimize data models that feed directly into the Microsoft Power Platform (Power BI). - Ensure data platform performance and reliability while maintaining a platform-agnostic approach where possible. Qualifications - Experience with Databricks: Proficiency with Spark, Delta Lake, and workspace management. - Knowledge of Microsoft Fabric: Experience navigating and implementing solutions within the Fabric ecosystem. - Architecture Design: Solid experience implementing Medallion Architecture. - Data Programming: Proficiency in Python (PySpark) and SQL for data transformation and engineering. Requirements - Cloud Ecosystem: Experience with the Azure platform and Azure Data Factory. - AI Integration: Familiarity with Vector Databases (e.g., ChromaDB) for LLM support. - Power Platform: Experience preparing data for Power BI and Power Apps consumption. - Domain Knowledge: Familiarity with industrial data or engineering calculations. - DevOps for Data: Experience with CI/CD for data pipelines and version control (Git). - Data Governance: Knowledge of data quality standards and metadata management. Company Description At Coforge, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

• Work on requirements gathering, backlog refinement, support for information architecture, technical documentation and collaboration with technical and business areas; • Development and maintenance of data solutions applied to the Climate context, involving integration, organization, processing and provisioning of information from various corporate and industrial sources; • Preparation and maintenance of technical documentation in Markdown, Word or SharePoint; • Development of data pipelines, support for information architecture, integration with SAP systems, APIs and databases, as well as structuring information for analysis, traceability and decision support; • Work in a multidisciplinary environment, with constant interaction between technical and business areas, participating in understanding requirements, refining needs, building solutions, producing technical documentation and monitoring deliveries throughout the development, evolution and maintenance lifecycle; • Integration and processing of data from REST APIs, RDBMS/NoSQL databases, SAP ERP/SAP Analytics environments and relational stores such as SQL Server, Oracle or PostgreSQL, using tools like SSMS, DBeaver, Postman and Insomnia;

• Definir e evoluir arquitetura de dados cloud-native na AWS; • Projetar modelos de dados otimizados para: consulta operacional (APIs) e consumo pelo agente conversacional; • Liderar migração de dados históricos do mainframe; • Garantir consistência, integridade e rastreabilidade; • Trabalhar com grandes volumes e dados históricos complexos; • Construir e manter pipelines de ingestão e transformação; • Integrar fluxo existente (PeopleSoft, mainframe, RDS); • Otimizar consultas no PostgreSQL; • Implementar estratégias de particionamento, indexação e caching (Redis); • Reduzir latência para consultas frequentes; • Garantir aderência a LGPD, políticas de retenção; • Implementar controle de acesso a dados, mascaramento / anonimização quando necessário, auditoria e lineage; • Estruturar dados para consumo por LLMs; • Apoiar criação de camadas semânticas; • Trabalhar com embeddings / indexação semântica (diferencial); • Implementar validações de qualidade de dados; • Monitorar pipelines e consistência;

• A specialist who will lead the creation and management of the data pipelines that feed the project's analytical models. • Responsible for ensuring the quality, integrity, and availability of data for training and inference of Machine Learning models in production. • Expected to mentor other team members and define data engineering and MLOps strategies.