Mark Pevec
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolMark Pevec is a multidisciplinary software engineer, architect, and instructor with over a decade of experience building cloud-native, data, and DevOps solutions across Google Cloud, AWS, and Azure. He blends hands-on systems work—from embedded realtime and telematics to microservices, Kubernetes, and service mesh—with deep data engineering expertise in Hadoop, Spark, and Dataflow. A pragmatic infrastructure-as-code and SRE practitioner, he routinely implements Terraform/Ansible-driven CI/CD, autoscaling, monitoring, and incident response for resilient distributed systems. Mark has contributed to GoogleCloudPlatform/DataflowTemplates, improving Elasticsearch integration and robustness for production data flows, reflecting a knack for practical open-source improvements. Trained in computer science and physics at the University of Toronto, he also mentors and teaches teams to translate complex requirements into secure, maintainable systems. He’s as comfortable debugging assembly or RTOS issues as he is shaping cloud migration and observability strategies.
10 years of coding experience
H.B.Sc. with high distinction, Computer Science & Physics, H.B.Sc. with high distinction, Computer Science & Physics at University of Toronto - University of St. Michael's College
Stackoverflow
121reputation
13kreached
3answers
2questions
Github Skills (20)
amazon-elasticsearch10
google-cloud-dataflow10
java10
javas10
elasticsearchquery10
aws-elasticsearch10
elasticsearch10
apache-beam10
elasticsearch-api10
javascript9
google-cloud-build6
google-cloud-pubsub6
docker6
google-cloud-dns6
bigtable6
Programming languages (12)
Github contributions (5)
GoogleCloudPlatform/DataflowTemplates
May 2022 - Aug 2022
Cloud Dataflow Google-provided templates for solving in-Cloud data tasks
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 reviews, 17 commits, 4 PRs in 3 months
Contributions summary:Mark primarily focused on enhancing the Elasticsearch integration within the Dataflow templates. Their contributions involved modifying the `ElasticsearchIO` class to support features like document upserts using index and the ability to extract index, ID, type, and delete properties from the document to be indexed using Javascript functions. The user also addressed retry behavior within the Elasticsearch integration to improve data processing robustness. They also implemented options to switch bulk request methods to index or create.
gcpgoogle-cloud-platformgoogle-cloudgoogle-cloud-storagedataflow
riskris/tf-gcp-project
Jan 2025 - Feb 2025
respistory for tf gcp project
Contributions:10 reviews, 3 PRs, 1 push in 1 month
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