About the Role:
Cybereason is seeking a Senior Data Infrastructure Engineer to architect and scale the data backbone that powers our cutting-edge cybersecurity analytics. In this role, you’ll build distributed systems that process billions of security events daily, powering our platform with real-time and historical threat intelligence. You’ll work at the intersection of big data, cloud-native engineering, and cybersecurity, ensuring our infrastructure can support advanced analytics and machine learning at scale.
Key Responsibilities:
- Design and develop petabyte-scale data infrastructure and real-time streaming systems capable of processing billions of events daily
- Build and optimize high-throughput, low-latency data pipelines for security telemetry
- Architect distributed systems using cloud-native technologies and microservices patterns
- Design and maintain data lakes, time-series databases, and analytical stores optimized for security use cases
- Implement robust data governance, quality, and monitoring frameworks across all data flows
- Continuously optimize for performance, scalability, and cost-efficiency in large-scale data workloads
- Collaborate with data science and security teams to enable advanced analytics and ML capabilities
- Ensure data infrastructure complies with strict security, availability, and compliance requirements
Required Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or related field
- 7+ years of experience building and maintaining large-scale data infrastructure
- Proven experience operating petabyte-scale systems processing billions of records per day
- Expert-level proficiency with stream processing: Apache Flink, Kafka, Pulsar, Redpanda, Kinesis
- Deep experience with analytical and time-series databases: ClickHouse, Druid, InfluxDB, TimescaleDB
- Familiarity with distributed storage: Hadoop (HDFS), Amazon S3, GCS, Azure Data Lake
- Strong skills in: Rust, Go, Scala, Java, or Python for high-performance systems
- Cloud expertise: AWS (EMR, Redshift, Kinesis), GCP (Dataflow, BigQuery, Pub/Sub), or Azure equivalents
- Solid experience with Kubernetes, Docker, and Helm; familiar with service mesh like Istio or Linkerd
- Strong grasp of data lake/lakehouse architectures and modern data stack tools
Preferred Qualifications:
- Experience with Apache Iceberg, Delta Lake, or Apache Hudi
- Familiarity with Airflow, Prefect, or Dagster for orchestration
- Knowledge of search platforms: Elasticsearch, OpenSearch, or Solr
- Experience with NoSQL: Cassandra, ScyllaDB, or DynamoDB
- Familiar with columnar formats: Parquet, ORC, Avro, Arrow
- Experience with observability stacks: Prometheus, Grafana, Jaeger, OpenTelemetry
- Familiar with Terraform, Pulumi, or CloudFormation for IaC
- GitOps tools: ArgoCD, Flux for automated deployments
- Exposure to data mesh, data governance, and metadata tooling (Apache Atlas, Ranger, DataHub)
- Background in cybersecurity, SIEM, or security analytics platforms
- Familiarity with ML infrastructure and MLOps best practices
Technical Skills and Knowledge:
- Stream Processing: Real-time analytics, windowing, state management, exactly-once semantics
- Distributed Systems: Partitioning, consistency, HA, failover, load balancing
- Data Lakes & Lakehouses: Multi-zone design, schema evolution, metadata management
- Cloud-Native Patterns: Microservices, event-driven design, auto-scaling, regional failover
- Performance Tuning: Query optimization, resource allocation, caching, compression
- Governance: Lineage tracking, anomaly detection, quality controls, regulatory compliance
- Security: Encryption, zero-trust principles, access control, audit logs
- Observability: Metrics, logs, distributed tracing, alerting
Key Competencies:
- Proven track record of building and scaling high-volume, high-throughput data systems
- Strong analytical and problem-solving skills in complex distributed environments
- Excellent communication and collaboration across cross-functional teams
- Self-driven with ability to manage multiple high-impact infrastructure initiatives
- Passionate about data architecture and staying ahead of emerging tech
- Experience mentoring engineers and shaping technical direction
What We Offer:
- Work on cutting-edge cybersecurity technology
- Collaborative and innovative environment
- Continuous learning opportunities
- Competitive salary and benefits
- Remote work options
About Cybereason
Cybereason provides unparalleled cyber-defense solutions. They use machine learning and AI to detect and analyze threats for workstations such as computers, mobile devices, and other digital gadgets.
Cybereason's latest offerings connect huge volumes of data to seamlessly automate detection and prevention of cyber-attacks.
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