$ whoami
$ cat role.txt
$ ls skills/
$ echo $STATUS
Processing 10M+ records daily | 99.9% accuracy | Supporting 1000+ users | 40% performance gains
> MS Data Science @ Regis University (GPA: 3.9) - Expected: Dec 2025
> BS Computer Science - Completed 2021
> 4+ Years Python & SQL Experience
> Location: Denver, CO | Work Authorization: No sponsorship required
Data Engineer with proven expertise building scalable ETL pipelines processing 10M+ records daily. Specializing in Apache Spark, AWS services (S3, Lambda, EC2, CloudFormation), and modern data architectures supporting 1000+ users with 99.9% accuracy and uptime.
Track record: 40% performance improvements, 75% reduction in processing time, 50% query optimization, and $50K+ saved in prevented downtime costs through proactive monitoring systems.
class GraduateDataEngineer: def __init__(self): self.role = "Graduate Data Engineer" self.company = "Regis University" self.duration = "Aug 2024 - Present" self.location = "Denver, CO" def achievements(self): return [ "Process 10M+ records daily with 99.9% accuracy", "Built ETL pipelines with 40% performance improvement", "Support analytics platform for 1000+ users", "Automated workflows reducing processing by 75%", "Implemented real-time & batch processing systems" ] def tech_stack(self): return ["Apache Spark", "Apache Airflow", "PySpark", "AWS CloudFormation", "Snowflake", "FastAPI"]class PythonDeveloper: def __init__(self): self.role = "Python Developer - Data Engineering Focus" self.company = "Appharu" self.duration = "Jan 2020 - Jul 2023" self.location = "Remote" def impact(self): return { "daily_processing": "5M+ records", "performance_gain": "50% query optimization", "cost_savings": "$50K+ prevented downtime", "uptime": "99.9% system availability", "user_base": "100+ business users" } def key_projects(self): return [ "Monitoring system preventing $50K+ downtime", "ETL pipelines for 5M+ daily records", "50% query performance improvements", "Automated data validation reducing manual work by 80%" ]Built enterprise-grade ETL pipeline processing 10M+ healthcare records daily with 99.9% accuracy. Implemented parallel processing achieving 40% performance improvement.
Infrastructure as Code solution using AWS CloudFormation, automating provisioning of S3, Lambda, EC2, and RDS. Reduced deployment time by 75%.
Interactive dashboard for 1000+ users. Integrated with Apache Airflow for automated reporting and Snowflake for analytics queries achieving sub-second response times.
Comprehensive testing framework with monitoring dashboards ensuring 99.9% system reliability. Adopted by 10+ developers.
@inproceedings{
title={"Predictive Models for Scope 3 Emissions: Improving Accuracy with Machine Learning and Financial Data"},
author={Dahal, S. and Pochampally, A. and Soraf, K.},
conference={Marketing and Data Sciences},
institution={Regis University},
year={2024}
}
$ ping sarak-dahal
Connection established!