About this role
Accountabilities:
- Design, develop, and implement efficient and scalable ETL/ELT data pipelines, integrating, transforming, and loading information from multiple relevant sources to support Credit Risk operations.
- Maintain and continuously update procedural manuals and area policies, incorporating changes to existing activities and documenting new processes and responsibilities as required.
- Monitor, maintain, and optimize the existing data infrastructure to ensure high availability, performance, security, and reliability while identifying and implementing continuous improvements.
- Implement and monitor data quality processes by defining validation rules, conducting data profiling, and ensuring the integrity and reliability of information used by Credit Risk teams, in alignment with organizational data governance policies and the requirements of Bacen Resolution No. 4.966/21.
- Provide technical support to Credit Risk teams in accessing and using data, helping identify data requirements, troubleshoot issues, and implement new data solutions that address business needs.
Requirements:
- Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field, providing a strong foundation in software and data engineering principles.
- Solid knowledge of cloud platforms, particularly AWS and GCP, with the ability to work with cloud-based data environments and infrastructure.
- Strong proficiency in SQL and Python for data processing, transformation, automation, and development of scalable data solutions.
- Solid knowledge of Big Data technologies, particularly Spark and Hadoop, with experience working with large-scale data processing environments.
- Knowledge of data orchestration tools, especially Airflow, and an understanding of how to manage and automate data workflows.
- Knowledge of Kubernetes and Linux, supporting the management and operation of modern data and technology environments.
- Previous experience in the financial or banking sector is considered a strong advantage, particularly for professionals familiar with regulated data environments and risk-related processes.
- Knowledge of MLOps is a valuable differentiator, particularly for candidates with experience supporting machine learning and data-driven solutions in production environments.
Benefits:
- Health insurance and dental insurance.
- Wellhub (Gympass) membership to support health, fitness, and wellbeing.
- Transportation allowance.
- Food and meal allowance.
- Access to Domo School, an educational platform, as well as partnerships with educational institutions.
- Private pension plan.
- Life insurance.
- Day Off.
- Extended maternity and paternity leave.
- Fully remote work model.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
#LI-CL1