I am a Data Analyst with over two years of experience delivering business intelligence initiatives, predictive analytics, and data pipeline improvements. I work with SQL, Python, PostgreSQL, Power BI, and cloud data platforms to turn complex data into actionable business insights.
I have improved user retention, streamlined data-preparation workflows, and increased reporting efficiency through analytical deep dives, data governance practices, and automated reporting solutions. My work focuses on producing reliable data products that support cross-functional teams and executive decision-making.
I am experienced in data cleaning, exploratory data analysis, KPI analysis, cohort and retention analysis, ETL development, and data lineage. I use Python libraries such as Pandas, NumPy, BeautifulSoup, Selenium, and Scikit-learn to automate workflows, collect data, and build predictive models.
I have worked on customer retention analytics, cloud-based ETL pipelines, web scraping, SQL ticket-triage automation, dashboard development, and churn prediction. My projects have included AWS S3, Redshift, BigQuery, PostgreSQL, Looker, and Power BI.
I transitioned from a Civil Engineering background into Data Analytics, bringing a structured problem-solving approach to data modeling and business analysis. I am motivated by opportunities to improve data quality, automate repeatable processes, and communicate analytical findings clearly to stakeholders.
I hold certifications in Google Data Analytics, AWS Cloud Practitioner, Databricks Data Engineering, and Deloitte job simulations in Data Analytics and Cybersecurity. I continue to broaden my technical specialization through practical analytics and cloud-data projects.
