Hi, I'm Rodrigo Steve

Bankaool

Big Data Team

Data governance, AI-powered automation, and data quality for a Mexican digital bank. Promoted from Data Analyst Intern.

• AI Solutions over the DWH: Build AI solutions over the Data Warehouse, including a RAG chatbot that lets analysts find data sources through natural-language queries instead of manual catalog searches.
• BI Governance Automation: Automate BI governance with Python pipelines over the Power BI Report Server REST API, mapping report-to-view lineage and identifying unused reports through usage analytics.
• Matching Engines: Develop matching engines (fuzzy and semantic, with embeddings) to map business rules to warehouse views and to triage access requests automatically.
• Transaction Reconciliation: Reconcile transactions between the core banking system and the Data Warehouse, surfacing and documenting discrepancies for remediation.
• Pricing & Regulatory Compliance: Validate pricing and regulatory compliance by testing account movements against rules auto-extracted from tariff documents.
• Ingestion QA with Airflow: Support DWH ingestion QA by orchestrating and validating Airflow DAG runs via REST API.
• Data Lineage & DDL Audits: Document source-to-target data lineage and audit DDLs across SQL Server and PostgreSQL environments.

Bankaool

Business Intelligence Audit Team

Automated data processes, built dashboards and handled regulatory requirements for a Mexican bank.

• Workflow Automation: Connected Power Automate with Jira via HTTP/JSON to standardize audit incident tracking. Improved existing flows by adding timestamps and fixing data reads.
• Python Pipelines: Developed scripts to transform, audit and compare datasets automatically, generating Excel/PDF reports. Included discrepancy detection between production and Data Warehouse.
• Geospatial Analysis: Designed the methodology for geolocating banking transactions by channel and state, producing monthly executive reports.
• Regulatory Compliance (AML): Generated alert databases, SPID transactional profiles, match verification, and handled CONDUSEF and REUNE requests with ad-hoc queries.
• Dashboards & BI: Built Power BI dashboards (Client Semaphore, R27 Dashboard) with operational KPIs. Large-scale SQL queries on PostgreSQL for historical audits.
• Data Validation: Identified and documented inconsistencies between production systems and DWH (BEL clients, acquirer risk, user-client congruence).