Senior Security Data Engineer, Toast

• We are looking for an experienced data engineer with a strong understanding of entity and user behavioral analysis, with an interest in hard data science and classical machine learning methods
• We will favor candidates with an independent mindset and a strong desire to discover attackers wherever they might hide
• 5+ years of experience in data engineering, with a strong focus on security data
• Proven experience building and maintaining large-scale data pipelines and systems
• Proficiency in Python and SQL
• Proficiency in Linux system administration and shell scripting
• Strong understanding of data warehousing solutions (e.g., Athena, Google BigQuery)
• Familiarity with ETL tools (e.g., Apache Spark) and data integration techniques
• Knowledge of security frameworks and standards, such as NIST, ISO, or CIS
• Experience with AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn, etc.) for data analysis and modeling
• Experience with Cloud platforms, specifically AWS, and its services
• Analytical Mindset: Ability to analyze complex data and draw actionable insights related to security risks and vulnerabilities
• Communication Skills: Excellent written and verbal communication skills, with the ability to convey complex concepts to both technical and non-technical audiences
• Independent Mindset: Ability to self-direct towards the solution to problems. Asynchronous collaboration through logging and documentation is a must

Desirable

• The Senior Security Data Engineer will be responsible for designing, prototyping, building, and maintaining robust data pipelines and systems that enhance our security and detection posture
• Data Pipeline Development: You'll design and implement efficient and scalable data pipelines that process security-related data, ensuring integrity and availability
• Security Integration: Collaborate with security, fraud, and abuse teams to integrate data sources relevant to threat detection, incident response, and compliance monitoring
• Threat Modeling: Propose and participate in experiments to explore how we might better unmask attackers in our systems, both in heuristic detections and unattended learning pipelines
• Data Modeling: Create and maintain data models that support our security analytics and reporting needs. Optimize queries for performance and usability
• Automation and Tooling: Develop automation tools to enhance our security data management and support data collection, analysis, and reporting processes
• Stay Current: Keep abreast of the latest trends and advancements in data engineering and security to ensure our strategies remain cutting-edge