Senior Data Engineer — Data Platform (Spark/Kubernetes)

Location: Tel Aviv, Israel; full-time R&D role.

Company

Cyera builds a unified security operating model for the age of AI, securing relationships between data, access, behaviors, systems, humans, and AI.

What you will do

• Design, deploy, operate, autoscale, and tune a Spark-on-Kubernetes compute platform with multi-tenancy support.
• Own the Apache Iceberg lakehouse storage layer and AWS Glue catalog, including table design, partitioning, compaction, schema evolution, and retention.
• Build and operate Airflow orchestration, including DAG standards, deployment flows, environment promotion, and reliability.
• Develop shared frameworks, libraries, templates, CI/CD flows, base images, and GitOps-based delivery for production-grade data workloads.
• Design scalable batch, streaming, and Kafka-based event-driven pipelines; lead large-scale backfills and migrations while maintaining data consistency.
• Monitor and harden the platform, respond to incidents, and optimize compute, storage, queries, job performance, and cloud cost visibility.

Requirements

• 5+ years of software engineering experience, including meaningful experience building and operating large-scale data platforms.
• Hands-on experience with distributed processing engines, especially Spark, including production performance tuning and debugging.
• Practical experience deploying and operating workloads in Kubernetes-based environments.
• Experience building shared frameworks, libraries, or internal tooling for other engineers, including clean APIs, documentation, versioning, and backward compatibility.
• Strong SQL and data modeling skills, including complex analytical queries, query tuning, and partitioning strategies.
• Solid software engineering fundamentals in Python and/or Scala or Java, including testing, code review, and CI/CD.
• Experience with cloud-native data infrastructure on AWS or an equivalent platform at high scale.
• Ability to own work from design through deployment, operation, and cost management, and make pragmatic decisions in a fast-changing environment with incomplete information.

Nice to have

• Experience with Apache Iceberg or other open table formats such as Delta Lake or Hudi, including compaction, schema evolution, and catalog management.
• Experience with EMR on EKS, Spark Operator, or similar Spark-on-Kubernetes setups.
• Experience with Airflow at scale, including custom operators, deferrable operators, and multi-environment deployment.
• Experience with Kafka-heavy or event-driven architectures and CDC pipelines using Debezium or similar tools.
• Experience with GitOps tooling and infrastructure-as-code.
• Experience with FinOps or cloud cost optimization for data workloads.
• Background in cybersecurity-related data infrastructure or compliance-constrained environments such as FedRAMP or GovCloud.
• Contributions to open-source data engineering tools or frameworks.

Culture & Benefits

• Ownership and autonomy to take initiative, move quickly, and turn ideas into impact.
• Opportunity to work at the technical heart of the data platform and solve problems at real scale.
• Collaborative environment focused on learning, raising engineering standards, and shared success.
• Diverse backgrounds, perspectives, and experiences are welcomed.