I’m a Financial Data Analyst and Mechanical Designer with over 5 years of technical experience across manufacturing, reporting, and business analytics. My work bridges engineering precision with financial insight—leveraging tools like Power BI, Excel, Python, and SQL to drive better decisions and optimize performance.
With internships in DevOps and Data Engineering, I've designed scalable ETL pipelines, automated CI/CD workflows, and deployed cloud-based monitoring solutions. I’m now focused on transitioning fully into a Data Engineering role, where I can build data infrastructure that powers advanced analytics and business intelligence.
To secure a full-time Data Engineering position by January 2026. My goal is to architect and maintain modern data pipelines that automate data flows, enhance accessibility, and deliver clean, actionable insights for high-impact teams.
Languages & Tools: Python (Pandas, NumPy, Seaborn, Matplotlib), SQL, Bash
Data Engineering: Apache Airflow, dbt, Spark, Kafka, Hadoop
Databases: PostgreSQL, Snowflake, AWS Redshift, Google BigQuery
Cloud & DevOps: AWS (S3, RDS), GCP (BigQuery), Prometheus, CI/CD
BI & Analytics: Power BI, Excel (Power Query, Macros, VBA), Tableau, DAX
Automation: Python scripting, Excel automation, API integration
🚢 Titanic Dataset Analysis:
A full ETL + analysis pipeline using Python and Jupyter Notebooks. Included data cleaning, transformation, feature engineering, and insights visualization.
If you're seeking a data-driven professional who blends business, engineering, and analytics—and is laser-focused on scaling into data engineering—I'm open to opportunities and collaboration.