• 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.

• drive significant business impact by shaping our data infrastructure • developing robust data models • promoting best practices in analytics engineering • collaborate closely with the Marketing team • ensure that funnels are being tracked and surfaced in our data models • evaluate Marketing campaigns using accurate data • create trusted, self-serve datasets • proactively identify and resolve data quality issues • add comprehensive metadata to datasets • partner with Marketing stakeholders to translate business questions into robust data products • enable Marketing teams to confidently use Omni and core GTM datasets • activate data from Snowflake into operational tools