Harlieen Bindra

A data engineer with 5+ years of experience, combining technical expertise with analytical acumen to solve complex problems and deliver impactful results. Experienced in developing scalable software solutions, with proven capability in handling big data analytics and data engineering to derive actionable business insights.

VCS/Banking and Finance Client — Senior Data Engineer

Sep 2024 – Current

• Reduced processing errors by 30% through streamlined workflows and implementing precise control measures.
• Boosted early intervention measures by 20% by creating data-driven models that identified high-risk defaulting accounts.
• Designed real-time reporting dashboards using Excel and Tableau, reducing flagging time for default accounts by 40%,enabling faster resolution.

University of Wisconsin — Graduate Assistant

Jan 2023 – Dec 2023

• Automated event registration and website content management using CMS tools (Ungerboeck, Ingenuix), reducing manual data entry tasks by 40% and increasing event processing capacity by 25%.

May 2021 – Dec 2022

• Developed scalable data pipelines with Python, SQL, Hive, and PySpark to analyze operational data from leading banks, mitigating risks and reducing associated losses by 25%.
• Resolved a critical issue of COVID-19 extension program exclusions, restoring 20% of impacted customer accounts and ensuring compliance with federal mandates.
• Improved credit risk management turnaround time by 40% by designing 15+ custom solutions, enhancing the accuracy of risk flagging systems.

Jun 2019 – Apr 2021

• Conducted ML-driven research with University of California, San Francisco faculty, creating predictive models for neonatal outcomes, reducing clinical data processing time by 30%.
• Processed NICU sensor data to enhance real-time monitoring, improving data availability by 30% and reducing data corruption risks by 40% through secure handling practices.
• Developed AI-powered models to analyze neonatal health metrics, enabling proactive interventions and improving clinical decision-making speed by 15%.

Jun 2018 – May 2019

• Automated finance assurance data processing with Python scripts, cutting manual effort by 25% and expediting report generation timelines by 30%.
• Conducted 10+ software IT audits, identifying vulnerabilities in system controls, leading to a 35% improvement in overall system security.

M.S. in Computer Science, University of Wisconsin

GPA: 3.96/4.0

[Honors: Recipient of Tuition Scholarship as a Graduate Assistant.]