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Bryan SchaeferBS
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Bryan Schaefer

@bryanschaefer3

Lead Data Engineer building scalable batch/streaming data platforms for analytics and machine learning.

United States
Message

What I'm looking for

I’m looking to lead the design and operations of reliable, scalable data platforms for analytics and ML. I want a team that values data reliability, observability, CI/CD, and strong collaboration with engineering, analytics, and data science.

I’m a Lead Data Engineer focused on architecting and operating scalable data platforms for analytics, reporting, and machine learning workloads. I design and implement batch and streaming pipelines, cloud-native data architectures, and distributed processing frameworks, delivering production-grade infrastructure with strong data modeling, orchestration, observability, and automated data quality controls.

In recent roles, I directed development of 30 batch and streaming pipelines processing 5TB daily datasets and integrated 12 data sources to deliver curated analytics datasets used by BI and data science teams. I established reliability and observability across 50 production pipelines, reducing failure rates by 35%, and mentored 6 engineers while improving deployment efficiency by 40% through CI/CD and modular pipeline standards.

Experience

Work history, roles, and key accomplishments

MI

Data Analyst Intern

MindEase

Jan 2017 - Jan 2018 (1 year)

Analyzed operational and product datasets using SQL and Python to support 10 internal reporting dashboards. Authored transformation scripts, created BI dashboards for 15 KPIs, and performed data validation checks that improved reporting accuracy by 20%.

Education

Degrees, certifications, and relevant coursework

Texas Tech University logoTU

Texas Tech University

Bachelor of Science in Computer Science, Computer Science

2013 - 2016

Grade: GPA 3.8/4.0

Completed a Bachelor of Science in Computer Science, building foundations in algorithms, data structures, distributed systems, and database systems. Applied Python, Java, and SQL through coursework and academic projects focused on data processing, software development, and backend system implementation.

Tech stack

Software and tools used professionally

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