Ernesto Casas
Senior QA Engineer at GFT Group
Costa Rica
Summary
🤩
Rockstar🎓
Top SchoolErnesto Casas is a Senior QA Engineer with 13+ years of experience building quality into complex systems across banking, brokerage, marketing automation and cloud platforms. He blends hands-on test automation (C#, Python, Selenium, Cypress, Xamarin) and CI/CD expertise with test strategy, metrics and release sign-off in Agile/SAFe environments. Ernesto has led QA teams and framework design for web, mobile, desktop and backend APIs while driving process improvements that tie testing to business outcomes. Notably, he contributed performance-critical PairHMM improvements and FPGA/AVX/OpenMP integrations to the widely used GATK genomics project, showing deep backend optimization skills beyond typical QA work. Certified across ISTQB, SAFe and Scrum, he pairs formal testing discipline with practical DevOps automation to shorten feedback loops and reduce production risk. Based in Costa Rica, he routinely collaborates with distributed teams to groom stories, align stakeholders and deliver reliable releases.
13 years of coding experience
18 years of employment as a software developer
Bachelor's Degree, Computer Science, Bachelor's Degree, Computer Science at Universidad de Costa RicaHigh School, High School at Sequoia High School, SF, CA, USA
English, Spanish
Github Skills (15)
avx10
bioinformatics10
javas10
excel-dna10
genome10
genomics10
gatk10
ng10
java10
genomes10
openmp9
python8
fpga7
tensorflow6
spark5
Programming languages (3)
Github contributions (5)
broadinstitute/gatk
Apr 2017 - Jun 2018
Official code repository for GATK versions 4 and up
Role in this project:
Backend Developer
Backend DeveloperContributions:13 commits, 12 PRs, 11 pushes in 1 year 1 month
Contributions summary:Ernesto significantly contributed to the GATK (Genome Analysis Toolkit) project by implementing and enhancing the PairHMM (Pair Hidden Markov Model) functionality. They focused on optimizing the PairHMM, introducing support for new interfaces, native arguments, and FPGA acceleration, leading to performance improvements. The user's work involved integrating and configuring native implementations like AVX and OpenMP for CPU acceleration, along with experimental FPGA support. They also modified the CNNScoreVariants module, improving input tensor preparation.
genomesciencednaspark-mlngs
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