Senior Data Engineer

About Cyera

Come join the company building the security operating model for the age of AI. AI has changed how data is used — and security must change with it. Cyera's mission is to empower businesses to accelerate AI Adoption by defining a holistic approach to securing AI - from data to access to model. Instead of perimeter controls and static policies, Cyera provides a unified control plane that understands relationships between data, access, and behaviors across humans, systems, and AI. Backed by the world’s leading investors and working with a large and growing list of Fortune 1000 companies, we are looking for world-class talent to join us as we usher in the new era of data and AI security.

About the Role

We’re looking for a Senior Data Engineer to join our Research Engineering Team and play a pivotal role in shaping the data infrastructure that powers Cyera’s innovative solutions. In this role, you’ll design, build, and scale data pipelines and data warehouses from the ground up, creating the backbone for the world’s leading data classification engine. You’ll collaborate closely with researchers and machine learning experts to enable the development of advanced models, ensuring a seamless flow of high-quality, reliable, and well-structured data. Your contributions will directly impact the scalability, performance, and precision of our platform, advancing our mission to protect critical data.

What You’ll Do

• Design and build scalable batch and streaming data pipelines that process complex datasets from diverse sources, enabling reliable and high-performance model training and inference.
• Collaborate closely with researchers and data scientists to deliver high-quality, structured datasets that accelerate experimentation and model iteration.
• Lead large-scale historical backfills and migration initiatives to ensure data consistency and integrity across evolving storage and compute platforms.
• Optimize data workflows through advanced query tuning, indexing, partitioning, and cost optimization strategies to support efficient large-scale analytics.
• Architect and maintain high-performance cloud-based data platforms using modern data stack components across AWS, GCP, or Azure.
• Operate distributed data processing engines to handle massive volumes of structured and unstructured data.
• Design and implement event-driven architectures using large-scale queue systems such as Kafka or SQS to ensure reliable and efficient data movement.