Senior Data Engineer

Architected and deployed fault-tolerant, high-performance data pipelines using Apache Spark and AWS Glue, accelerating analytics delivery by 60%. Led migration from legacy systems to a cloud-native architecture, reducing operational costs by $1.2M annually. Translated business needs into scalable data models driving multimillion-dollar growth initiatives. Automated ETL workflows processing over 15TB/day with 99.9% uptime. Mentored 8+ engineers and introduced a centralized data governance framework that improved compliance by 85%.

Delivered real-time support and root-cause analysis for reporting systems, reducing downtime by 70%. Resolved data discrepancies across platforms, improving accuracy by 95%. Implemented validation scripts reducing manual review by 60%. Optimized SQL procedures, cutting report generation time from 15 to 3 minutes. Translated stakeholder issues into technical fixes, increasing satisfaction by 40%.

Built ML models using Python and Scikit-learn that boosted customer retention by 35%. Processed 100M+ records using SQL and R, enabling predictive insights. Designed A/B testing frameworks, increasing conversion by 22%. Created dashboards (Power BI/Tableau) for KPI tracking. Led full-cycle DS projects contributing $2.5M+ in revenue growth.

Built scalable data pipelines with Python and Airflow, improving delivery speed by 55%. Integrated 10TB+ data from APIs, flat files, and cloud sources into a unified lake. Optimized ETL and SQL logic, reducing compute costs and processing time by 70%. Deployed data warehouses with Snowflake and Redshift. Supported ML and analytics teams with reliable data flows.

Expected to complete in 12/2025

Completed in 2025

Completed in 2024