Role Description Minimum 8+ years of experience working as Data Modeler for AWS Cloud Data warehouse. - Work in an Agile environment and participation in scrum daily standups, sprint planning reviews and retrospectives. - Understand project requirements and translate them into technical solutions which meet the project quality standards. - Ability to work in a team in a diverse/multiple stakeholder environment and collaborate with upstream/downstream functional teams to identify, troubleshoot and resolve data issues. - Strong problem solving and good analytical skills. - Excellent verbal and written communication skills. - Experience and desire to work in a global delivery environment. - Stay up to date with new technologies and industry trends in development. Qualifications - Must have Data Modeling Experience on Data Warehouse & Analytics solution. - ER Studio. - Cloud experience preferred. - Strong Data Modeling Experience in Banking Domain. - Exposure to cloud technologies – Snowflake, Redshift, Apache Flink and Apache Kafka. Company Description

• Auxiliar na validação do uso de novas tecnologias e soluções técnicas • Apoiar na execução de processamento de grande volume de dados através de ferramentas e arquiteturas projetadas • Acompanhar projetos focados na melhora da qualidade de dados • Apoiar no provento de insumos para novos produtos da empresa.

• Own the planning, estimation, and delivery of data engineering tasks and projects, partnering with project management to track progress, milestones, and risks. • Design and implement scalable data pipeline solutions by analyzing customer data, identifying patterns, and performing data profiling to reverse engineer schemas and APIs. • Build, optimize, and maintain high-performance data pipelines, delivery networks, and bi-directional data integrations between customers and partners. • Ensure data reliability and quality through robust development of automated unit, integration, and regression tests. • Troubleshoot complex data and integration issues, providing escalated technical support and driving root cause resolution. • Collaborate cross-functionally to deliver integration solutions, and provide technical guidance, training, and documentation to internal teams and stakeholders. • Monitor data pipeline health, proactively identify performance or reliability issues, and implement improvements to ensure system stability. • Contribute to best practices in data engineering, including code quality, testing standards, and scalable architecture.

• Design, build, and maintain scalable and reliable data pipelines and data processing systems • Develop and optimize ETL/ELT processes for batch and real-time data ingestion • Ingest and process data from internal systems, APIs, third-party platforms, and streaming sources • Design and maintain data models, data lakes, and data warehouse structures • Write clean, efficient, and reusable code for large-scale data processing • Collaborate with product managers, data teams, and engineering teams to understand business needs and translate them into data solutions • Ensure data quality, integrity, and availability across systems • Monitor, troubleshoot, and resolve production issues related to data pipelines and workflows • Contribute to improving code quality, system reliability, and best engineering practices • Actively participate in Agile ceremonies (standups, sprint planning, retrospectives)