Senior Technical Lead Resume Example

Senior Technical Lead · Envestnet | Yodlee

Chicago, IL · 2022 - Present

• Led a team of 7 engineers in designing and implementing a new real-time data ingestion platform for financial market data, reducing latency by 45% and increasing processing throughput by 200% to handle 5TB daily.
• Architected and deployed a multi-cloud (AWS, Azure) microservices solution using Kubernetes and Apache Kafka, improving system resilience and achieving 99.99% uptime for critical investment analytics services.
• Drove the adoption of modern DevOps practices, including CI/CD pipelines (Jenkins, ArgoCD) and Infrastructure-as-Code (Terraform), resulting in a 30% reduction in deployment time and a 25% decrease in production incidents.
• Mentored junior and mid-level engineers, fostering a culture of technical excellence and contributing to a 15% improvement in team code quality metrics and a 10% increase in developer productivity.

Technical Lead, Data Platforms · Capital One

McLean, VA · 2018 - 2022

• Led the technical direction and implementation for a data governance platform, integrating data lineage and quality checks across 15+ internal data sources, ensuring compliance with evolving financial regulations.
• Optimized existing data warehousing solutions by migrating legacy ETL processes to Apache Spark on AWS EMR, reducing daily batch processing times from 6 hours to 1.5 hours and saving $50K annually in compute costs.
• Collaborated with product owners and stakeholders to define technical requirements and roadmaps for data-driven features, impacting over 2 million retail banking customers.
• Introduced A/B testing frameworks for data-driven product features, enabling faster iteration and contributing to a 5% uplift in user engagement metrics for personalized financial insights.

Senior Software Engineer · Quantifi Analytics

New York, NY · 2016 - 2018

• Developed high-performance data processing modules in Java and Python for a financial analytics platform, improving query response times by an average of 30% for large datasets.
• Contributed to the design and implementation of RESTful APIs for real-time data access, facilitating integration with client-facing applications and increasing API adoption by 20% within the first year.
• Implemented robust unit and integration testing frameworks, reducing software bugs by 18% in critical production environments.

Master of Science in Computer Science · Carnegie Mellon University

Pittsburgh, PA · 2014 - 2016

• Specialization in Distributed Systems and Software Engineering.
• Relevant Coursework: Advanced Distributed Systems, Cloud Computing, Database Management Systems, Machine Learning in Production.
• Thesis: 'Optimizing Transactional Latency in Geographically Distributed NoSQL Databases', earning 'Distinction'.

Bachelor of Science in Computer Science · University of Illinois Urbana-Champaign

Urbana, IL · 2010 - 2014

• Graduated Magna Cum Laude, GPA: 3.8/4.0.
• Relevant Coursework: Algorithms and Data Structures, Operating Systems, Computer Architecture, Networks, Discrete Structures.
• Developed a real-time stock market simulator for capstone project using Java and Apache Kafka for data streaming.

Senior Technical Lead · Amazon Web Services (AWS)

Seattle, WA · 2023 - Present

• Led a team of 7 engineers in the development and launch of a new real-time data streaming platform, processing over 10TB of data daily with 99.99% uptime.
• Architected and implemented a scalable microservices infrastructure using AWS Fargate and Kubernetes, resulting in a 30% reduction in operational costs and 2x faster deployment cycles.
• Mentored junior and mid-level engineers, fostering a culture of technical excellence and contributing to a 20% improvement in team productivity metrics.
• Drove critical system performance optimizations, decreasing API response times by an average of 40ms for key services under peak load.
• Championed adoption of CI/CD best practices, automating deployment pipelines and reducing manual release effort by 60%.

Software Engineer II · Salesforce

San Francisco, CA · 2020 - 2022

• Developed and maintained core backend services for enterprise CRM solutions, handling over 1 million requests per minute with an average latency of 50ms.
• Contributed to the migration of legacy systems to a modern, cloud-native architecture, achieving a 15% improvement in system reliability and scalability.
• Implemented robust unit and integration testing frameworks, reducing production bugs by 25% across assigned modules.
• Collaborated with cross-functional teams to define API specifications and integrate new features, impacting 500,000+ daily active users.

System Architect / Hardware Engineer · Intel Corporation

Austin, TX · 2016 - 2018

• Designed and optimized embedded system architectures for high-performance computing platforms, increasing processing efficiency by 18%.
• Led the integration of hardware components with custom firmware, reducing system boot time by 10% and improving overall stability.
• Developed automated testing scripts for hardware validation, decreasing manual testing cycles by 40% and accelerating product time-to-market.

Master of Science in Computer Science · University of Washington

Seattle, WA · 2018 - 2020

• Specialized in Distributed Systems and Cloud Computing, completing projects on microservices architecture and serverless deployments.
• Thesis: 'Optimizing Container Orchestration for High-Throughput Data Pipelines,' achieving a 15% reduction in processing latency.
• Coursework included Advanced Algorithms, Machine Learning Systems, and Cloud Infrastructure Design.
• Graduated with a 3.9/4.0 GPA.

Bachelor of Science in Electrical Engineering · University of Texas at Austin

Austin, TX · 2012 - 2016

• Focused on embedded systems design and signal processing, applying principles to develop efficient hardware-software interfaces.
• Completed capstone project: 'Low-Power IoT Sensor Network for Environmental Monitoring,' reducing power consumption by 25%.
• Awarded 'Dean's List' for 6 semesters; graduated with High Honors (3.8/4.0 GPA).
• Relevant coursework: Digital Signal Processing, Microcontroller Systems, VLSI Design.