Jersey City, New Jersey, United States
Data Analyst with 4+ years of experience delivering data-driven solutions in cloud and analytics environments. I specialize in building scalable data pipelines, transforming large-scale data, and creating dashboards that support business decision-making. Core Skills: • Python, SQL • AWS, Azure, Snowflake, BigQuery • Power BI, Tableau, Looker What I’ve worked on: • ETL pipelines and data warehousing • Large-scale (TB-level) data processing • Predictive analytics, A/B testing, and forecasting Impact: • Improved data processing efficiency and reporting performance • Enhanced data quality and accuracy • Delivered actionable insights for product and business teams I enjoy solving business problems with data and collaborating across teams. Open to Data Analyst opportunities in cloud and data-driven environments.
• Engineered an automated operational data transformation pipeline using Python (Pandas) and AWS Lambda to ingest flight delay telemetry and baggage tracking logs into S3, reducing end-to-end data latency by 35% and improving missing bag traceability across major US hubs. • Engineered SQL-based analytical models within Snowflake to perform exploratory data analysis (EDA) and data wrangling on high-volume passenger booking data, optimizing seat allocation strategies and boosting Revenue per Available Seat Mile (RASM) by 4.2%. • Developed predictive maintenance and component failure models utilizing Random Forest and time-series forecasting via Scikit-learn, achieving a 91% accuracy rate in predicting repair part demand and reducing unscheduled aircraft downtime by 14%. • Designed executive Tableau dashboards integrated with automated Jira workflows to track daily flight load factors and turnaround bottlenecks, giving stakeholders cross-functional visibility that cut delay-response resolution times by 22%.
• Optimized ETL pipelines using SSIS, Azure Data Lake, and Databricks to ingest and process 3TB+ multi-source data, improving data availability by 45% and reducing pipeline failures by 30%. • Architected and implemented data integration workflows across NoSQL (MongoDB) and relational systems to support enterprise data warehousing, increasing query performance by 35% and enabling faster analytics delivery. • Established predictive models using Python, Scikit-learn, and Keras (regression, decision trees, time-series forecasting) to analyze customer behavior and revenue trends, improving forecast accuracy by 28%. • Performed NLP-based text analysis on customer feedback and support logs to identify key sentiment drivers, reducing churn indicators by 20% through actionable insights for business teams. • Delivered interactive Looker dashboards and conducted cohort analysis to track user retention and lifecycle metrics, improving stakeholder visibility by 40% while managing version control and collaboration through GitHub, Confluence, and Slack.
• Analyzed large datasets using Python (Pandas, NumPy) and SQL Server to identify data quality issues and business trends, improving dataset accuracy by 30% and enabling reliable downstream analytics. • Cleaned and transformed 1M+ records using Python and advanced Excel (Power Query, VBA), reducing data inconsistencies by 35% and accelerating reporting turnaround time by 25%. • Developed and validated classification models in Python to segment customers and predict outcomes, increasing model performance (accuracy/F1-score) by 20% through feature engineering and tuning. • Executed A/B and hypothesis testing on product features and campaigns, increasing conversion rates by 15% through data-driven insights. • Crafted Power BI dashboards and time-series forecasting models to monitor KPIs and demand trends, enhancing business visibility by 40% while maintaining version control using Git.