Senior Data Developer (Databricks, Spanish)
New
C
CI&TFinancial data
Listing location: Brazil; Workplace type: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Espanhol para interação com equipes globais, discussões técnicas e documentação.
- Required Skills
- PythonSQLDatabricksPySpark
Requirements
- Bring solid experience with Databricks, including notebooks, jobs, workflows, clusters, and practices for building scalable data solutions.
- Use PySpark, Python, and SQL to transform raw data into reliable, organized datasets.
- Have experience with Delta Lake and Lakehouse architecture, including Delta tables, incremental processing, data optimization, and structured data layers.
- Have knowledge of the Azure data ecosystem, especially Azure Data Lake Storage, data integration patterns, and cloud data processing.
- Have experience supporting financial data pipelines; experience with reconciliation, P&L, financial close, or other business-critical processes is preferred.
- Have hands-on experience maintaining and improving existing pipelines, including troubleshooting, performance optimization, documentation, and production support.
- Be able to work with structured and semi-structured data while ensuring quality, traceability, and readiness for analytics and business use.
- Be able to investigate complex data, identify root causes, and propose practical solutions.
- Have interest or experience in AI models and advanced data capabilities.
- Use Spanish for interaction with global teams, technical discussions, and documentation.
Responsibilities
- Develop, maintain, and improve data pipelines using Databricks, PySpark, Python, and SQL.
- Integrate new data sources into the existing data ecosystem.
- Support financial data reconciliation, P&L routines, and global close processes.
- Process structured and semi-structured data and optimize transformations, storage formats, and query performance using Delta Lake and Lakehouse practices.
- Monitor and troubleshoot existing pipelines, investigate discrepancies, and support reliable daily and monthly processing.
- Improve performance, scalability, maintainability, and data quality across the financial data ecosystem.
- Collaborate with data engineers, business stakeholders, finance teams, and technology teams to deliver data solutions.
- Support data governance, validation rules, auditability, documentation, and controls.
- Support initiatives involving AI models and advanced data capabilities.
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