Compensation Overview

About The Role

At Abnormal Security, we are on a thrilling mission to safeguard the world’s largest enterprises against a vast range of relentless email and collaboration application cyber security attacks. Our relentless pursuit involves crafting an exceptional suite of products that empowers customers to seamlessly visualize, expertly control, and fearlessly combat cyber-security threats. 

This position represents a unique opportunity to contribute significantly to our mission of providing unparalleled detection capabilities worldwide. The team’s mission revolves around empowering our detection team with high-quality data and cutting-edge tools, ensuring that all misclassifications can be easily assessed and analyzed. The strategic approach encompasses the development of essential detection tools, improved misclassification comprehension, and enhanced communication with customers.

Core components the team owns include the rule based email detection engine, analysis tools for misclassification understanding and flow comprehension, data quality for ML model analysis and training and the platform for customer interaction of detection concerns.

What You Will Do 

• Enhance the speed and quality of detection systems development.
• Power pipelines to process and respond to customer feedback responsibly.
• Supply detection teams with top-notch, accurate data used for evaluating and training ml models.
• Automate processes to improve customer responses.
• Develop, design, modify, and test systems for enhanced data quality and understanding.
• Collaborate with Technical Program Managers, Product Managers, Data Engineers, Data Scientists, and operational and engineering teams to implement, verify, and iterate on product development.
• Exercise sound judgment in selecting methods and techniques for problem-solving.
• Writes code with testability, readability, edge cases, and errors in mind.
• Writes and reviews technical design documents.

Must Have

• Proficient in Python, Databricks, Airflow, Django, and Postgres.
• Demonstrated experience in building data pipelines using PySpark.
• Familiarity with large-scale solutions or environments that involve complex integrations, demanding latency requirements, or significant throughput challenges.
• Adept at performance debugging and benchmarking to ensure efficient operation of applications.
• Production level experience with technologies such as PySpark, Data platform and Data coordination, Hadoop, Hive, and data processing frameworks.
• Proven ability to translate business requirements into detailed software requirements, effectively articulating systems design to technical and non-technical stakeholders alike.
• Experience in identifying, analyzing, and resolving complex technical issues, demonstrating a methodical approach to troubleshooting and problem-solving.
• Proven experience working effectively with cross-functional teams, demonstrating the ability to collaborate with a diverse range of stakeholders.

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