The Role

We're seeking a Senior Data Engineer to build and maintain the ML/AI data infrastructure powering our email security platform. In this role, you'll design and optimize scalable data pipelines that enable threat detection and investigation while supporting both machine learning models and LLM-powered agents that provide context-aware security insights.

You'll work on our Detection Intelligence Platform (DIP) building feature engineering frameworks, and offline/online feature stores that serve as the foundation for ML model research and context engineering for AI agents. You'll collaborate with data scientists, ML engineers, and security researchers to build data models and context stores that power our detection systems and enable human security analysts to investigate threats effectively.

Key Responsibilities:

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  Develop and maintain scalable data pipelines on AWS/Azure using technologies such as Spark, Airflow, Athena, Kubernetes etc. to process structured and unstructured email data at scale

• Design and optimize Iceberg-based data lake tables and schemas for efficient storage, querying and versioning across petabyte-scale datasets distributed across data centers globally

• Build and manage feature engineering frameworks that support offline batch processing and online real-time feature serving for ML model training and inference

• Develop and maintain training data pipelines optimized for distributed ML model training, ensuring data lineage and reproducibility

• Collaborate with data scientists and security researchers to understand data requirements and translate them into robust, production-grade data solutions

• Monitor and optimize data pipeline performance, implementing observability and alerting to ensure data freshness and quality

• Mentor junior engineers and foster a culture of engineering excellence and knowledge sharing

Required Experience:

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  Several years of industry experience building and maintaining distributed data systems and high-scale data pipelines in a managed cloud environment (AWS / Azure / GCP) using big data processing engines such as Spark, Flink, Dask, Ray, Beam, DataBricks Workflows or similar

• Deep proficiency in Python for developing production-grade data processing code

• Strong experience with Infrastructure-as-Code frameworks, particularly Terraform

• Solid understanding and hands-on experience with open table formats for data lakes (Apache Iceberg, Hudi, DeltaLake) and data modeling best practices

• Experience with AWS Athena, Glue, or similar data query and cataloging services

• Experience with Apache Airflow or similar workflow orchestration tools for batch and real-time pipeline management

• Demonstrated ability to design and implement scalable ETL/ELT pipelines handling complex data transformations

• Excellent communication skills and ability to collaborate effectively with technical and non-technical stakeholders

Good to have:

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  Experience with feature engineering frameworks and feature stores (e.g., Feast, Tecton, or custom solutions)

• Familiarity with Kubernetes for containerized data workloads and orchestration

• Background in building data infrastructure for machine learning and AI applications

• Experience with data quality frameworks and observability tools for data pipelines