• Design, develop, test and deploy data integration solutions using IICS (Cloud Data Integration, Application Integration, Data Quality, Data Synchronization). • Build ETL/ELT pipelines to ingest, transform, and deliver data between cloud and onprem systems (SaaS, databases, data lakes, data warehouses). • Implement secure and performant integration patterns using IICS mapping tasks, mappings, tasks, and parameterization. • Develop and maintain reusable templates, mappings, and components to accelerate delivery. • Collaborate with architects, data engineers, analytics teams, and business stakeholders to define integration requirements and data contracts. • Troubleshoot and resolve production incidents; perform root cause analysis and implement fixes. • Automate deployments and CI/CD for IICS artifacts (Dev → Test → Prod), including versioning, changelogs, and release notes. • Implement monitoring, alerting, and operational runbooks for integrations. • Enforce data quality rules and profiling; work with business to remediate data quality issues. • Prepare technical documentation: design documents, runbooks, data flow diagrams, and support guides. • Support platform governance: connectivity, security, access controls, best practices, and cost optimization.

• Azure Data Engineering: Design and maintain data pipelines and solutions using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Data Lake, ensuring end-to-end reliability, scalability, and performance. • Analytics & Visualization: Build dashboards and analytical reports in Power BI and Tableau, translating infrastructure metrics, application performance data, and business KPIs into concrete, actionable recommendations. • Data Modeling & Integration: Develop data modeling, data mining, and integration processes across on-premises and cloud sources, creating reliable datasets to support analytical models and dashboards. • Queries & Large-Scale Processing: Write complex SQL and Kusto queries, as well as scripts and REST API calls for large-scale data collection and processing. • Cloud Cost Management: Operate with cost awareness in managing cloud compute and storage resources, proposing optimizations that balance performance and efficiency. • Technical Reference: Guide application and business teams on data usage and interpretation, proactively promoting best practices in data engineering, modeling, and BI tooling. • Agile Collaboration: Work within Agile/Scrum methodologies using project management tools such as Jira, with the ability to prioritize deliverables and manage tasks autonomously across onsite and offshore teams.

• Lead the migration and structuring of data from the Credit, Collections and Fraud Prevention areas to Google Cloud Platform (GCP) • Build refined tables and automate processes using the various tools available in GCP • Oversee system integration projects focused on process automation and data structuring • Generate new insights from data analysis and communicate findings clearly to business stakeholders • Monitor the performance of Collections & Fraud (C&F) ML models through automated data processes • Create innovative processes using GenAI and Predictive AI within the Directorate

• Design, build, and own end-to-end data solutions for Core products. • Establish and scale analytics foundations by implementing data pipelines. • Oversee the design and development of interactive analytical experiences. • Mentor and support the growth of junior team members through coaching.