Company
Banking
EngenheirodeDadosAWSSênior-Databricks
Neural analysis suggests this role is
optimal for Senior candidates.
“Engenheiro de Dados AWS Sênior- Databricks. Skills: Data Engineering, Databricks, AWS. Analyze data sources. Map data sources”
Industry & Context.
Analytical thinking; Problem-solving
What They're Looking For.
Must Have
4+ years Data Engineering, 3+ years Databricks enterprise, Advanced AWS knowledge, Advanced Python proficiency, Advanced SQL proficiency, Experience with data modeling, Experience with Delta Lake architecture, Experience with data warehousing concepts, Experience with ETL/ELT processes, Experience designing distributed data architectures, Experience implementing high-performance pipelines, Experience with data mapping, Experience with data migration projects, Experience with legacy data integration, Experience with heterogeneous data integration, Ability to lead technical deliveries, Make architectural decisions, Make engineering decisions, Focus on scalability, Focus on performance optimization, Focus on governance, Focus on security, Focus on data quality, Excellent communication skills, Excellent collaboration skills, Excellent stakeholder management skills
Nice to Have
Experience supporting regulatory reporting, Experience in financial institutions, Experience in banking sector, Experience in financial services sector, Experience in regulated industries sector, Databricks certifications, Familiarity with DevOps practices, Familiarity with infrastructure automation, Familiarity with CI/CD pipelines, Experience with Power BI, Experience with MicroStrategy, Knowledge of Agile delivery frameworks, Knowledge of Scrum, Knowledge of Kanban, Experience mentoring junior professionals, Experience mentoring mid-level professionals
What You'll Do.
Identify data structures
Identify data relationships
Identify business rules
Identify integration requirements
Ensure data integrity
Ensure data traceability
Design ETL/ELT pipelines
Develop ETL/ELT pipelines
Maintain ETL/ELT pipelines
Implement business rules
Implement data transformations
Support reporting processes
Build regulatory reports
Validate regulatory reports
Optimize regulatory reports
Contribute to data architecture
Support data solution design
Ensure governance compliance
Ensure security compliance
Ensure privacy compliance
Ensure data quality standards
Troubleshoot technical issues
Resolve technical issues
Provide technical leadership
Collaborate with stakeholders
Collaborate with technical teams
How You'll Work.
Team & Collaboration
Business stakeholders; Regulatory teams; Multidisciplinary technical teams
Communication Scope
Stakeholder management
Process & Methodology
Agile delivery frameworks, Scrum, Kanban
Full Job Description
## Accountabilities Analyze and map data sources, identifying structures, relationships, business rules, and integration requirements. Lead the migration of raw and legacy data into Databricks environments, ensuring data integrity, traceability, and quality throughout the process. Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, Delta Lake, Python, SQL, and AWS services. Implement business rules and data transformations required to support operational and regulatory reporting processes. Build, validate, and optimize regulatory reports while ensuring compliance with established standards, controls, and deadlines. Contribute to data architecture decisions and support the design of modern cloud-based data solutions within the AWS and Databricks ecosystem. Ensure compliance with governance, security, privacy, and data quality standards across all stages of the data lifecycle. Troubleshoot and resolve complex technical issues related to data platforms, pipelines, and cloud infrastructure. Provide technical leadership, mentoring, and knowledge sharing to less experienced team members. Collaborate closely with business stakeholders, regulatory teams, and multidisciplinary technical teams to deliver high-quality solutions. Requirements 4+ years of professional experience in Data Engineering, including proven experience in senior-level roles. Strong hands-on experience with Databricks, preferably with at least 3 years working on enterprise-scale implementations. Advanced knowledge of AWS cloud services and Databricks integration within AWS environments. Solid expertise in data modeling, Delta Lake architecture, data warehousing concepts, and ETL/ELT processes. Advanced proficiency in Python and SQL for large-scale data processing and transformation. Experience designing and implementing distributed data architectures and high-performance processing pipelines. Practical experience with data mapping, migration projects, and integration of legacy or he
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