Principal Data Engineer, SpyCloud

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related STEM field
• 12+ years of experience in data engineering or closely related disciplines
• 2+ years in a technical leadership or principal engineering role
• Experience building and operating Lakehouse or large-scale distributed data lake architectures in AWS
• Knowledge of distributed systems as it pertains to data storage and computing
• Hands-on experience preparing and managing data for machine learning and LLM workflows
• Strong development skills in Python and Go, especially in building data-intensive systems
• Practical experience using DynamoDB and other AWS-native data services at scale
• Effective communication and collaboration across technical and non-technical stakeholders
• Proven ability to mentor and grow engineering talent and lead large-scale initiatives

Desirable

• As a Principal Data Engineer at SpyCloud, you will lead the architectural transformation of our data systems, working closely with Product Management, SpyCloud Labs, and Engineering teams
• Your focus will be on modernizing our data platform into a scalable, AWS-native Lakehouse architecture, building purpose-built data stores, and preparing data pipelines optimized for machine learning and large language models (LLMs)
• This is a hands-on, strategic leadership role shaping the future of data at SpyCloud
• Redesign and modernize data architecture for scalability, performance, and AI-readiness
• Build and optimize real-time and batch data pipelines using AWS tools (e.g, Lambda, S3, DynamoDB)
• Design systems to prepare and serve high-quality data for ML models and LLM applications
• Optimize cost and performance of daily data ingestion, transformation, and storage workflows
• Work with Product and Engineering teams to ensure data solutions meet product requirements
• Evaluate and implement modern tooling for data processing, ML data workflows, and observability
• Guide and mentor data engineers, championing best practices and technical excellence
• Ensure operational data quality, security, and reliability across systems
• Lead long-term data architecture planning aligned with company and AI-driven goals