Lead Data Analyst driving data-driven decision making and organizational insights. Specializing in stakeholder collaboration, dashboard development, and data quality frameworks. Expert in SQL optimization, real-time analytics, and emerging AI technologies for automated reporting and enhanced privacy in data processing.

• Collaborated closely with stakeholders to deliver dashboards and insights, improving decision-making effectiveness by 40%.
• Designed and implemented a real-time data quality framework monitoring 100+ metrics, improving data health visibility across the organization.
• Optimized SQL queries and PostgreSQL processes, reducing execution times by 30% and ensuring reliable insights delivery.
• Documented reporting processes and governance guidelines, aligning analysis outputs with compliance standards.
• Pioneered integration of local LLMs (Google MedGemma) to automate summarization and enhance reporting privacy.

Data Engineer focused on building scalable ETL pipelines and data infrastructure. Specialized in Python and SQL development, data warehouse architecture, and implementing reliable data orchestration workflows with high uptime requirements.

• Designed and deployed ETL pipelines in Python and SQL, automating ingestion of 10M+ financial and operational records monthly, cutting manual data preparation by 60%.
• Built and maintained data warehouse structures (PostgreSQL & Cloud SQL) that supported real-time analytics for 5 business units, reducing reporting delays by 40%.
• Implemented data orchestration workflows with Docker, improving deployment reliability and achieving 99.5% pipeline uptime across environments.
• Developed monitoring and alerting systems for pipelines, enabling 30% faster incident resolution and proactive issue detection.

Data Analyst focused on process automation, data quality initiatives, and cross-functional collaboration. Specialized in Python and SQL automation, dashboard design, and building organizational data literacy.

• Automated key reporting processes using Python and SQL, reducing reporting turnaround by 50% and improving accessibility for operational teams.
• Implemented data quality monitoring initiatives, cutting data errors by 20% and strengthening confidence in insights.
• Collaborated across teams to define KPIs and design dashboards, improving campaign targeting efficiency.
• Trained non-technical users in data interpretation, enhancing organizational data literacy and resilience in analytics adoption.

Business Intelligence Analyst specializing in market research, competitive analysis, and data modeling. Focused on delivering strategic insights for business growth and maintaining robust data infrastructure for actuarial and business analysis.

• Conducted in-depth market research and competitive analysis to identify market trends, customer preferences, and potential opportunities, providing valuable insights for strategic planning and business growth.
• Developed and maintained data models, cubes, and metadata documentation, enabling efficient ad-hoc analysis.
• Produced market research and competitive analysis reports that informed strategic planning and product development.

Dual-source OHLC + real-time market data pipeline with Python, PostgreSQL, and SQLAlchemy. Features market-aware scheduling, structured logging, and performance tracking with automated validation and JSONB metadata storage.

Comprehensive financial data analysis toolkit using Python, Pandas, NumPy, and Matplotlib. Automated data collection, statistical analysis, and interactive visualizations for financial market insights.

Interactive dashboards exploring content patterns and labor market trends. Features KPI tiles, time-series analysis, and comparative views published to Tableau Public.

End-to-end ML deployment using Random Forest algorithm in Django application. Containerized with Docker, featuring web interface for loan approval predictions and model performance tracking.