Job Description for Job Posting
About the Role
Abnormal Security is looking for a Senior Software Engineer to join the Detection Team. The Detection Division is focused on building the world’s most advanced technology for identifying and stopping email and cloud-based attacks that were previously undetectable and help make the world a safer place. As a Senior Software Engineer focused on building systems for Detection’s Signal Platform, you will be responsible for making feature development at Abnormal a fast, responsive, stable, and confident experience for our ML and Data Science team.
The ideal candidate would have the following qualities:
- A first principles approach to building scalable, customer-centric solutions
- A drive to solve meaningful & pragmatic problems for real-world people
- An ownership and impact oriented outlook on your efforts and growth
- An ability to iterate in real-time-solving novel problems, quickly and autonomously
- An ability to iterate in real-time - solving novel problems, quickly and autonomously
What you will do
- Architect, design, build, and deploy backend ETL jobs and infrastructure that support a world-class Detection Engine
- Ownership projects that enable us to meet ambitious goals for, such as building the plan to scale components of Detection’s Data Pipeline by 10x
- Own real-time, near real-time streaming pipelines and online feature serving services
- Collaborate closely with MLE and Data Science teams, by distilling feedback, correlating it to our strategy, and executing
- Coach and mentor junior engineers via 1on1s, pair programming, high quality code reviews and design reviews
Must Haves
- 5+ years of experience as a data engineer or in a similar role, with hands-on experience in building data-focused solutions.
- Expertise in ETL, data pipeline design, and data engineering tools and technologies (e.g., Apache Spark, Hadoop, Airflow, Kafka)
- Experience with maintaining real-time and near real-time data pipelines or streaming services at high scale
- Experience with maintaining large scale distributed systems on cloud platforms such as AWS, GCP, or Azure, including a strong grasp of best practices in cloud-based data engineering.
- A background in implementing data quality frameworks including validation, monitoring, and anomaly detection to ensure data accuracy, consistency, and reliability. You know how to design checks and alerts to maintain data integrity and quickly respond to potential issues.
- Proven ability to collaborate effectively with cross-functional teams, including data scientists, machine learning engineers, product managers, and other stakeholders. You’re able to translate requirements into actionable technical tasks, communicate progress clearly, and adapt to feedback.
- Excellent problem-solving skills and the ability to work independently in a fast-paced environment. You’re capable of breaking down complex challenges into manageable steps and iterating on solutions, balancing immediate needs with long-term scalability.
Nice to Have
- Familiarity with machine learning workflows and requirements to support data science teams effectively.
- Experience with streaming data architectures and real-time processing.
- Knowledge of security and compliance frameworks as they relate to data engineering and data privacy.
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