Data analytics, automation and engineering
Iām the Senior Operations Analyst and leader of the Automations Team at National Debt Relief. I lead the design and implementation of data engineering, analytics, and automation solutions that improve operational visibility and decision-making.
I oversee a small but high-impact team of analysts and developers who build and maintain internal tools, automation scripts, and reporting systems across multiple departments.
My focus is on automations, data infrastructure and engineering integrating data sources, orchestrating ETL pipelines, and building dashboards and APIs that drive insight and efficiency.
Before transitioning into data and automation, I worked as an Call Center Representative, English Teacher and Web Developer, which strengthened my communication, leadership, and problem-solving skills.
I hold certifications in Data Analytics & Engineering, Web Development, and Education. This foundation helps me guide projects that merge technical rigor with clarity, collaboration, and impact.
Iām currently advancing into Data Engineering and Cloud Infrastructure, studying ETL pipeline architecture, data orchestration, and cloud-based automation.
I lead and contribute to projects that connect data engineering, analytics, and automation, transforming raw data into scalable, accessible insight.
Developed an analytics platform integrating multiple operational data sources. Building ETL pipelines with Python and SQL, automated reporting workflows, and delivering dashboards for operational metrics.
Built a simulated IoT data pipeline generating factory sensor telemetry like temperature, vibration, and energy consumption. Streamed data via MQTT and Kafka, processed with Spark Structured Streaming, and stored in PostgreSQL. Developed dashboards and anomaly detection in Streamlit and Grafana.
Developed a Python-based debt snowball calculator to help users plan and prioritize debt repayments. Built with Streamlit for an interactive web interface and Flask APIs for backend simulation. Utilized Pandas and Matplotlib for data processing and dynamic visualizations that provide actionable insights for debt management.