Developed and deployed cloud-based Sisense dashboards improving a platform used by clients to identify high-performing loan officers and real estate agents for recruiting and partnership opportunities. Led the migration of 5 Tableau dashboards and data models to Sisense, improving performance, enabling faster dashboard load times, and supporting larger datasets for scalable analytics. Applied best practices for deployments across environments (development, UAT, production), ensuring quality, scalability, and controlled releases. Built strong client relationships to understand business needs and translate them into clear technical requirements and delivery time estimates.

Developed dashboards on the Sisense Periscope Data platform, providing operational performance visibility for multiple clients in the food industry and supporting data-driven decision making. Automated the production of highly customized client reports by integrating Sisense with Python, replacing manual form-based reporting with dynamic dashboards that visually represented clients' production layouts. Solved complex scoring and calculation challenges to deliver tailored analytics solutions that improved data clarity and reporting efficiency for key customers. Developed product demonstrations and analytics prototypes that contributed to the successful acquisition of new client contracts. Maintained and enhanced data integration and ingestion pipelines using Python and SQL, leveraging GitHub for version control and collaboration with other developers.

For a Canadian bank, developed and implemented automated solutions for regulatory reporting of credit risk parameters under the IRB approach (PD, LGD, and EAD), ensuring data traceability, data quality, and regulatory compliance. Drove the adoption of Power BI to visualize key indicators, including detailed breakdowns of risk parameters, trend analysis, and analytical insights. Integrated Python-based processes to interact with an API and automate data ingestion, as well as to validate data by reconciling it against multiple source systems. Significantly reduced production and validation timelines for multiple regulatory and management reports. Planned and managed weekly objectives for a team of analysts, assigned and tracked tasks using JIRA. In a leadership role (Reporting Manager), conducted performance reviews and set periodic objectives. Contributed to internal proof-of-concept (PoC) initiatives involving unstructured data ingestion and machine learning solutions using Python.

For a North American bank, developed business intelligence solutions to automate financial crime and compliance controls, including high-risk customer identification, KYC/ CDD/ EDD processes, global sanctions screening, and other AML compliance reporting. Designed and delivered dashboards and self-service analytics tools primarily using Tableau and Excel VBA, enabling our business partners to monitor key risk indicators. Built scheduled and automated data extraction processes from multiple databases, integrating data using SQL and SAS to support compliance reporting. Produced ad-hoc reports (quick data pulls) by extracting and reconciling data from various databases (SQL Server, Teradata, DB2), applying complex business rules.

Prepared operational risk incident reports, including fraud events and other risk-related incidents.