• Design, build, and maintain scalable data pipelines (batch and streaming) • Develop and optimize data models in a modern cloud data warehouse • Build robust ELT/ETL workflows and manage orchestration (e.g., Airflow, Dagster, or similar) • Partner with analytics, product, and engineering teams to deliver clean, trusted datasets • Implement data quality checks, monitoring, and alerting across pipelines • Handle sensitive data responsibly and help uphold privacy and compliance standards (e.g., HIPAA-aligned practices) • Contribute to the evolution of the platform's overall data architecture

• Design, develop and implement efficient, scalable data pipelines (ETL/ELT) to integrate, transform and load data from multiple sources relevant to the Credit Risk area; • Continuously update the department's Procedures Manual and Policy, including changes to existing tasks and the addition of new activities; • Monitor, maintain and optimize the existing data infrastructure to ensure availability, performance and security, making adjustments and implementing continuous improvements; • Implement and monitor data quality processes by defining validation rules, performing profiling and ensuring the integrity and reliability of data used in the Credit Risk area, in alignment with the organization's data governance policies and Bacen Resolution No. 4,966/21; • Provide technical support to the Credit Risk team for data access and usage, collaborating to identify data needs, resolve issues and implement new data solutions.

• Build and maintain batch and streaming ETL/ELT pipelines that feed a cloud data warehouse • Develop, schedule, and monitor cloud data jobs, diagnosing failures and tuning for cost and performance • Write idempotent load logic that handles reprocessing and backfills without creating duplicate data • Integrate external data providers and APIs with authentication, error handling, and retries • Implement data quality checks, validation, and alerting to catch pipeline issues early • Manage and deploy pipelines using infrastructure-as-code and CI/CD • Support data migrations and service updates with minimal downtime • Deliver clean, analytics, model-ready datasets for data scientists and analysts

• Being a Senior Data Engineer in this context means being the strategic pillar that connects infrastructure robustness with the company’s continuous telemetry stream. • You don’t just connect point A to point B; you design scalable, secure, and auditable ecosystems, ensuring that behavioral data generated by products arrives with high quality, low latency, and full compliance. • You operate at the intersection of Software Engineering and Data Architecture, transforming complex product requirements into resilient platforms based on Infrastructure as Code (IaC), proactive monitoring, and end-to-end governance.