Carlos Páez
Data Platform Tech Lead
Malaga, Andalusia, Spain
Summary
👤
Senior🎓
Top SchoolCarlos Páez is a Data Platform Tech Lead with eight years of experience building and operating cloud-native data infrastructure, currently leading platform efforts at Fever from Málaga, Spain. He started as a Python backend engineer and evolved into a Data/DevOps hybrid, specializing in running high-performance Airflow on Kubernetes, tuning clusters to handle >100k task runs per day and optimizing DAGs for idempotency and resource efficiency. Carlos combines hands-on IaC (Terraform), GitOps (Helm, ArgoCD), CI/CD and observability (Datadog) with pragmatic cost optimizations in Snowflake and migrations to DBT and cloud registries. An active contributor to the widely used Apache Airflow project, he has improved Helm charts and usability aspects of the web UI, reflecting a focus on deployability and developer experience. He enjoys cross-functional collaboration, mentoring junior engineers, and experiments with home automation and a self-managed Kubernetes cluster in his spare time.
8 years of coding experience
6 years of employment as a software developer
Grado en Ingeniería, Ingeniería informática, Grado en Ingeniería, Ingeniería informática at Budapest University of Technology and EconomicsGrado en Ingeniería, Ingeniería informática, Grado en Ingeniería, Ingeniería informática at Universidad de GranadaBachillerato, Tecnológico, Bachillerato, Tecnológico at IES Pedro Espinosa
English, Spanish, French
Github Skills (15)
apache-airflow10
kubernetes10
automation10
automations10
docker10
helm10
dockers10
kubernetes-pods10
cicd9
orchestration9
orchestra9
workflow-engine9
python8
data-pipelines8
data-pipeline8
Programming languages (11)
Github contributions (5)
apache/airflow
Jan 2022 - May 2024
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
DevOps Engineer
DevOps EngineerContributions:8 reviews, 9 PRs, 63 comments in 2 years 3 months
Contributions summary:Carlos's contributions primarily focused on enhancing the deployment and configuration aspects of the Apache Airflow project, specifically within its Helm chart. They implemented features such as setting revision history limits and adding global volume mounts to various components. Furthermore, the user addressed issues related to the webserver configuration and applied changes to improve the usability of the task instance view within the UI. This work likely involved automating processes and maintaining infrastructure-related settings.
monitorpythonschedulerapacheprogrammatically
csp33/airflow
Jul 2022 - Apr 2024
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Contributions:99 pushes, 22 branches in 1 year 9 months
monitorapacheprogrammaticallyapache-airflowairflow
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