Senior Data Engineer (Cybersecurity)

TL;DR

Senior Data Engineer (Spark/Python): Building and maintaining the ML/AI data infrastructure for an email security platform with an accent on scalable data pipelines and feature engineering frameworks. Focus on designing Iceberg-based data lakes, optimizing petabyte-scale datasets, and supporting LLM-powered agents for threat detection.

Location: Cordoba, Argentina

Company

Global leader in human- and agent-centric cybersecurity protecting Fortune 100 companies and millions of organizations worldwide.

What you will do

• Develop and maintain scalable data pipelines on AWS/Azure using Spark, Airflow, and Kubernetes to process email data at scale.
• Design and optimize Iceberg-based data lake tables and schemas for petabyte-scale datasets distributed globally.
• Build feature engineering frameworks supporting offline batch processing and online real-time feature serving for ML models.
• Develop training data pipelines optimized for distributed ML model training, ensuring data lineage and reproducibility.
• Collaborate with data scientists and security researchers to translate requirements into production-grade data solutions.
• Mentor junior engineers and foster a culture of engineering excellence and knowledge sharing.

Requirements

• Industry experience building distributed data systems and high-scale pipelines in cloud environments (AWS, Azure, or GCP) using Spark, Flink, or similar.
• Deep proficiency in Python for developing production-grade data processing code.
• Strong experience with Infrastructure-as-Code frameworks, particularly Terraform.
• Hands-on experience with open table formats for data lakes (Apache Iceberg, Hudi, DeltaLake).
• Experience with AWS Athena, Glue, or similar data query and cataloging services.
• Proficiency with Apache Airflow or similar workflow orchestration tools for pipeline management.

Nice to have

• Experience with feature stores such as Feast or Tecton.
• Familiarity with Kubernetes for containerized data workloads.
• Background in building data infrastructure specifically for machine learning and AI applications.
• Experience with data quality frameworks and observability tools for pipelines.

Culture & Benefits

• Competitive compensation and comprehensive benefits package.
• Flexible work environment.
• Annual wellness and community outreach days.
• Global collaboration and networking opportunities.
• Recognition culture and support for career success on your own terms.