Victor Delaroche

Objective: Standardize healthcare operational data and build a modern analytics platform to support Finance and HR teams. Data Lakehouse Architecture & Modeling • Designed and implemented a Lakehouse architecture on Microsoft Fabric, following the Bronze/Silver/Gold model. • Developed robust data pipelines using PySpark, T-SQL, and Spark SQL to handle ingestion, cleaning, and modeling of multi-source data. Dashboarding & BI Enablement • Built Power BI semantic models and dashboards tailored to Finance and HR use cases. • Worked closely with business teams to ensure KPIs and aggregations matched operational reality. Notebook Refactoring & Governance • Refactored legacy notebooks to improve modularity and maintainability. • Helped enforce naming conventions and implement data quality checks for stronger governance. Impact: Delivered a scalable and auditable data platform, enhancing operational reporting and enabling faster, more reliable decision-making. Tech Stack: Microsoft Fabric, Synapse, Power BI, PySpark, T-SQL, Spark SQL, Notebooks

Objective: Industrialize fraud detection and reconciliation workflows on healthcare claims data to support faster and more accurate investigations. Fraud Detection & Automation • Led fraud detection initiatives, identifying suspicious reimbursement patterns and provider behaviors. • Built scalable alerting pipelines in dbt, fully deployed on AWS with DuckDB for fast, auditable, and cost-efficient analytics. Reconciliation & Data Matching • Developed advanced reconciliation logic across internal and external datasets. • Reduced manual effort by automating data matching and validation workflows. Cross-Team Collaboration & Advanced Analytics • Acted as the main interface between fraud ops, analysts, and engineers to align detection logic with business needs. • Refined alerting strategies based on real-case investigations and contributed to improving fraud models accuracy. Impact: Accelerated fraud detection, improved traceability, and empowered business teams with actionable alerts. Tech Stack: DBT, JupyterLab, AWS, Looker Studio, DuckDB.

Objective: Strengthen marketing analytics and improve data-driven decision-making for franchise performance. Marketing Analytics & Business Intelligence • Built real-time dashboards to monitor franchise conversion funnels. • Developed custom KPIs to measure digital campaign impact and guide strategic marketing. Data Infrastructure & Process Optimization • Standardized data collection processes across franchises, ensuring data consistency. • Designed automated data pipelines to centralize marketing performance data. Data Team Support & Knowledge Sharing • Provided guidance to the data team on structuring datasets and analytics best practices. • Collaborated with stakeholders to improve data literacy and decision-making. Impact: Enabled data-driven marketing strategies, improving franchise conversion rates. Tech Stack: MySQL, Dynamics 365, Looker, Power BI.

Objective: Optimize marketing budgets and improve ROI for the Canadian and Mexican markets. Marketing Data Consolidation & Cleaxning • Gathered and unified multi-source marketing data (budgets, campaign performance, sales impact). • Cleaned and structured datasets to ensure consistency and reliability for analysis. Budget Optimization & Scenario Analysis • Developed data-driven budget reallocation models to maximize marketing efficiency. • Presented multiple budget scenarios to the Marketing Director, enabling better decision-making. Impact: Improved budget allocation efficiency, leading to higher ROI on marketing campaigns. Tech Stack: Python, Databricks, Snowflake, DBT, Power BI, Pandas, Scikit-Learn.

Objective: Optimize conversion funnel performance and improve customer acquisition through data. Product Analytics & Data Roadmap • Managed data strategy for 7 product squads, aligning business and data goals. • Designed and implemented tracking systems for new features and user behavior analysis. Business Intelligence & Dashboarding • Developed custom dashboards for product and business teams to track key performance metrics. • Created a recommendation engine for paid features, improving user engagement. Cross-Team Collaboration & Leadership • Assisted PMs in enhancing their data skills, promoting data-driven product decisions. • Led recruitment efforts and engaged in research projects related to machine learning and data engineering. Tech Stack: GCP, AWS, Terraform, BigQuery, BigQuery ML, Airflow, Jupyter Notebook, Machine Learning, DataStudio, Pandas, Scikit-Learn. Impact: Impact: Strengthened data-driven decision-making across product teams, enhancing feature adoption and business performance.

Objective: Optimize conversion funnel performance and improve customer acquisition through data. Conversion Rate Optimization (CRO) & User Behavior Analysis • Analyzed and optimized each stage of the conversion funnel, improving customer retention • Identified key bottlenecks and recommended data-driven UX improvements Business Intelligence & Dashboarding • Built custom dashboards to track marketing and conversion metrics, supporting growth strategies • Provided insights that guided A/B testing and marketing campaign optimizations Tech Stack: MySQL, DataStudio, Jupyter Notebook, Google Analytics. Impact: Improved conversion rates and user engagement, enhancing customer acquisition strategies.