Gabriel Franco Josino da Silva

Allocated to F1rst/Santander | Financial MIS

• Develop and maintain PySpark data pipelines in Databricks for financial data processing and internal business consumption.
• Create and maintain SQL business rules for fixed-income products and trade finance, including CRA, CRI, debentures, data treatment, transformation, validation, and parameterization.
• Build Python automations for SQL parameterization and validation using config-driven/metadata-driven patterns, reducing repetitive manual work and operational risk.
• Support modernization of legacy financial rules by mapping sources, fields, SQL dependencies, and validation/parity strategies.
• Validate schemas and troubleshoot Spark/Databricks pipeline issues, including data types, write behavior, and consistency between DataFrames and target tables.
• Support modernization of Spark data processes from on-premise environments to Azure Databricks and cloud lakehouse platforms using Delta Lake.
• Work with Oracle/Exadata integrations and maintain data processing routines connected to financial calculation drivers.
• Maintain, monitor, and troubleshoot batch jobs with Control-M, including job status tracking, log analysis, dependency checks, FileWatcher routines, and operational support.
• Map batch orchestration concepts from Control-M to Apache Airflow patterns such as DAGs, sensors, retries, dependencies, logs, and success markers for portfolio development.
• Work with corporate versioning and delivery flows using Git, branches, pull requests, GitHub Actions CI validation, and controlled deployments across environments.
• Collaborate with POs, backoffice teams, and business stakeholders to define, validate, and document data rules.

Insurance and Assistance Platform

• Queried, treated, and joined datasets in Databricks using SQL and PySpark across silver and gold data layers.
• Built Power BI dashboards connected to Databricks to support insurance business indicators and management reporting.
• Developed Python automations for text and Excel file processing, reducing manual preparation before dashboard consumption.
• Cleaned, standardized, and normalized datasets involving products, regions, payers, debtors, and credit analysis.
• Supported internal analytics requests using Databricks, on-premise query environments, Jira, and Pipefy.

Financial Data Pipelines and Business Rules

Development and maintenance of SQL and PySpark routines for financial data treatment, parameterization, validation, and delivery to internal teams in a banking environment, including fixed-income products, trade finance, config-driven rule processing, schema validation, and traceability improvements.

Technologies: SQL, Spark SQL, PySpark, Databricks, Delta Lake, Python, Oracle/Exadata, Control-M

Airflow Orchestration Transition - Portfolio Track

Study and project track to translate production batch concepts from Control-M into Apache Airflow, including DAG authoring, sensors, retries, task dependencies, monitoring, and failure handling.

Technologies: Apache Airflow, Docker, Python, GitHub Actions, CI/CD, data validation

Certification Simulator App - Portfolio Project

Active prototype for certification practice, built with Next.js, TypeScript, Tailwind, and Supabase. The next planned step is to add analytics around attempts, weak topics, learning progress, and data modeling.

Technologies: TypeScript, Next.js, Supabase/PostgreSQL, product analytics roadmap