Senior Data Infrastructure Engineer at Cybereason

📍 Job Overview

Job Title: Senior Data Infrastructure Engineer
Company: Cybereason
Location: Tokyo, Tōkyō, Japan
Job Type: On-site
Category: Data Infrastructure Engineer
Date Posted: 2025-06-18
Experience Level: 10+ years
Remote Status: On-site

🚀 Role Summary

Key Responsibilities: Design and develop petabyte-scale data infrastructure and real-time streaming systems, optimize for performance, scalability, and cost-efficiency, ensure data infrastructure complies with strict security, availability, and compliance requirements.
Required Skills: Expert-level proficiency with stream processing, analytical databases, distributed storage, high-performance programming languages, and cloud expertise.

📝 Enhancement Note: This role requires a deep understanding of large-scale data infrastructure and real-time streaming systems, with a strong focus on security, availability, and compliance.

💻 Primary Responsibilities

Data Infrastructure Design & Development: Design and develop petabyte-scale data infrastructure and real-time streaming systems capable of processing billions of events daily.
Data Pipeline Optimization: Build and optimize high-throughput, low-latency data pipelines for security telemetry.
Distributed Systems Architecture: Architect distributed systems using cloud-native technologies and microservices patterns.
Data Store Management: Design and maintain data lakes, time-series databases, and analytical stores optimized for security use cases.
Data Governance & Monitoring: Implement robust data governance, quality, and monitoring frameworks across all data flows.
Collaboration & Knowledge Sharing: Collaborate with data science and security teams to enable advanced analytics and ML capabilities, and mentor engineers to shape technical direction.

📝 Enhancement Note: This role requires strong analytical and problem-solving skills in complex distributed environments, with a proven track record of building and scaling high-volume, high-throughput data systems.

🎓 Skills & Qualifications

Education: Bachelor’s degree in Computer Science, Engineering, or related field.

Experience: 7+ years of experience building and maintaining large-scale data infrastructure.

Required Skills:

Stream processing: Apache Flink, Kafka, Pulsar, Redpanda, Kinesis
Analytical and time-series databases: ClickHouse, Druid, InfluxDB, TimescaleDB
Distributed storage: Hadoop (HDFS), Amazon S3, GCS, Azure Data Lake
Programming languages: Rust, Go, Scala, Java, Python
Cloud expertise: AWS (EMR, Redshift, Kinesis), GCP (Dataflow, BigQuery, Pub/Sub), or Azure equivalents
Kubernetes, Docker, and Helm; familiarity with service mesh like Istio or Linkerd
Strong grasp of data lake/lakehouse architectures and modern data stack tools

Preferred Skills:

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
Familiarity with columnar formats: Parquet, ORC, Avro, Arrow
Experience with observability stacks: Prometheus, Grafana, Jaeger, OpenTelemetry
Familiarity 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

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience with stream processing, analytical databases, and distributed storage.
Showcase data pipeline optimization and real-time streaming system design.
Highlight data governance and monitoring frameworks implementation.
Display proficiency in cloud-native technologies and microservices patterns.

Technical Documentation:

Provide code samples and documentation demonstrating high-performance system development.
Showcase data store management and optimization techniques.
Include data governance and monitoring framework implementation details.

📝 Enhancement Note: This role requires a strong portfolio demonstrating expertise in large-scale data infrastructure, real-time streaming systems, and data governance.

💵 Compensation & Benefits

Salary Range: ¥12,000,000 - ¥15,000,000 per year (Based on industry standards for senior data infrastructure engineers in Tokyo)

Benefits:

Competitive salary and benefits package
Remote work options
Continuous learning opportunities
Collaborative and innovative environment
Work on cutting-edge cybersecurity technology

Working Hours: Full-time, 40 hours per week, with flexible hours for deployment windows and maintenance.

📝 Enhancement Note: The salary range is based on regional market research, considering the high level of expertise required for this role and the cost of living in Tokyo.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cybersecurity software development and threat intelligence.

Company Size: Medium to large (500-10,000 employees)

Founded: 2012

Team Structure:

Cross-functional data infrastructure team, collaborating with data science, security, and engineering teams.
Flat hierarchy with a strong emphasis on collaboration and innovation.

Development Methodology:

Agile development methodologies, with a focus on continuous integration and deployment.
Regular code reviews, testing, and quality assurance practices.
Data-driven decision-making and continuous improvement.

Company Website: Cybereason

📝 Enhancement Note: Cybereason's culture emphasizes collaboration, innovation, and continuous learning, with a strong focus on data-driven decision-making and cutting-edge technology.

📈 Career & Growth Analysis

Web Technology Career Level: Senior Data Infrastructure Engineer, responsible for designing and optimizing large-scale data infrastructure and real-time streaming systems, with a strong focus on security, availability, and compliance.

Reporting Structure: Reports directly to the Director of Data Infrastructure, with a flat hierarchy and strong cross-functional collaboration.

Technical Impact: Directly impacts the performance, scalability, and security of Cybereason's cutting-edge cybersecurity analytics platform, powering real-time threat intelligence and advanced analytics.

Growth Opportunities:

Technical leadership and mentoring opportunities within the data infrastructure team.
Potential expansion into emerging technologies and data mesh architecture.
Potential career progression into a Principal or Staff Data Infrastructure Engineer role.

📝 Enhancement Note: This role offers significant growth opportunities for technical leadership and mentoring, with a strong focus on emerging technologies and data mesh architecture.

🌐 Work Environment

Office Type: Modern, collaborative office space with a strong focus on innovation and cross-functional collaboration.

Office Location(s): Tokyo, Japan

Workspace Context:

Modern, well-equipped workspace with multiple monitors and testing devices available.
Collaborative workspace with a strong emphasis on knowledge sharing and technical mentoring.
Cross-functional collaboration with data science, security, and engineering teams.

Work Schedule: Full-time, 40 hours per week, with flexible hours for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: Cybereason's work environment emphasizes collaboration, innovation, and cross-functional collaboration, with a strong focus on knowledge sharing and technical mentoring.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone Screen: Assessment of stream processing, analytical databases, and distributed storage proficiency.
On-site Technical Deep Dive: Detailed discussion of data infrastructure design, optimization, and governance.
Behavioral and Cultural Fit Interview: Assessment of problem-solving skills, collaboration, and cultural fit within Cybereason's data infrastructure team.
Final Review: Review of technical and behavioral assessments, with a focus on long-term fit and growth potential.

Portfolio Review Tips:

Highlight stream processing, analytical databases, and distributed storage projects.
Showcase data pipeline optimization and real-time streaming system design.
Include data governance and monitoring framework implementation details.
Tailor the portfolio to Cybereason's data infrastructure team and cutting-edge cybersecurity technology focus.

Technical Challenge Preparation:

Brush up on stream processing, analytical databases, and distributed storage concepts.
Practice data pipeline optimization and real-time streaming system design exercises.
Prepare for data governance and monitoring framework implementation questions.

📝 Enhancement Note: Cybereason's interview process focuses on technical depth, problem-solving skills, and cultural fit within the data infrastructure team, with a strong emphasis on long-term fit and growth potential.

🛠 Technology Stack & Web Infrastructure

Stream Processing Technologies:

Apache Flink, Kafka, Pulsar, Redpanda, Kinesis

Analytical and Time-Series Databases:

ClickHouse, Druid, InfluxDB, TimescaleDB

Distributed Storage:

Hadoop (HDFS), Amazon S3, GCS, Azure Data Lake

Programming Languages:

Rust, Go, Scala, Java, Python

Cloud Expertise:

AWS (EMR, Redshift, Kinesis), GCP (Dataflow, BigQuery, Pub/Sub), or Azure equivalents

Containerization & Orchestration:

Kubernetes, Docker, Helm, Istio, or Linkerd

Data Governance & Monitoring:

Apache Atlas, Ranger, DataHub, Prometheus, Grafana, Jaeger, OpenTelemetry

📝 Enhancement Note: Cybereason's technology stack emphasizes stream processing, analytical databases, distributed storage, and cloud expertise, with a strong focus on data governance and monitoring.

👥 Team Culture & Values

Data Infrastructure Values:

Win As One: Collaborate effectively with cross-functional teams to achieve common goals.
Ever Evolving: Embrace continuous learning and adapt to emerging technologies and best practices.
Daring: Push the boundaries of data infrastructure and real-time streaming systems to enable cutting-edge cybersecurity analytics.
Obsessed with Customers: Ensure data infrastructure meets the needs of Cybereason's customers and users.
Never Give Up: Persistently optimize and improve data infrastructure performance, scalability, and security.
UbU: Foster an inclusive and diverse team environment that accepts and values individual perspectives.

Collaboration Style:

Cross-Functional Integration: Collaborate closely with data science, security, and engineering teams to enable advanced analytics and ML capabilities.
Code Review Culture: Encourage peer programming and knowledge sharing within the data infrastructure team.
Knowledge Sharing: Facilitate technical mentoring and continuous learning opportunities within the team.

📝 Enhancement Note: Cybereason's data infrastructure team values collaboration, continuous learning, and innovation, with a strong focus on customer obsession and technical excellence.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Design and develop petabyte-scale data infrastructure and real-time streaming systems capable of processing billions of events daily.
Optimize data pipelines and real-time streaming systems for high throughput and low latency.
Ensure data infrastructure complies with strict security, availability, and compliance requirements.
Implement robust data governance, quality, and monitoring frameworks across all data flows.

Learning & Development Opportunities:

Technical Skill Development: Expand expertise in stream processing, analytical databases, and distributed storage technologies.
Emerging Technologies: Stay up-to-date with emerging data infrastructure trends and best practices.
Leadership Development: Develop technical leadership and mentoring skills within the data infrastructure team.
Architecture Decision-Making: Gain experience in data mesh architecture and data governance tooling.

📝 Enhancement Note: Cybereason's technical challenges and learning opportunities focus on data infrastructure design, optimization, and governance, with a strong emphasis on emerging technologies and leadership development.

💡 Interview Preparation

Technical Questions:

Stream Processing: Real-time analytics, windowing, state management, and exactly-once semantics.
Distributed Systems: Partitioning, consistency, HA, failover, load balancing, and data partitioning strategies.
Data Lakes & Lakehouses: Multi-zone design, schema evolution, metadata management, and data governance.
Cloud-Native Patterns: Microservices, event-driven design, auto-scaling, regional failover, and data partitioning strategies.
Performance Tuning: Query optimization, resource allocation, caching, compression, and data compression techniques.
Governance: Lineage tracking, anomaly detection, quality controls, regulatory compliance, and data governance best practices.
Security: Encryption, zero-trust principles, access control, audit logs, and data privacy regulations.
Observability: Metrics, logs, distributed tracing, alerting, and performance monitoring best practices.

Company & Culture Questions:

Cybereason's Data Infrastructure Team: Collaboration, innovation, and continuous learning within the data infrastructure team.
Cybereason's Technology Stack: Stream processing, analytical databases, distributed storage, and cloud expertise.
Cybereason's Culture: Customer obsession, technical excellence, and cutting-edge cybersecurity technology.

Portfolio Presentation Strategy:

Live Demonstration: Showcase stream processing, analytical databases, and distributed storage projects with live demos and responsive design.
Code Explanation: Explain code quality, architecture decision reasoning, and data governance implementation details.
User Experience Showcase: Demonstrate user experience design and interface development for data infrastructure projects.

📝 Enhancement Note: Cybereason's interview preparation focuses on technical depth, problem-solving skills, and cultural fit within the data infrastructure team, with a strong emphasis on long-term fit and growth potential.

📌 Application Steps

To apply for this Senior Data Infrastructure Engineer position at Cybereason:

Customize Your Portfolio: Highlight stream processing, analytical databases, and distributed storage projects, showcasing data pipeline optimization, real-time streaming system design, and data governance implementation details.
Optimize Your Resume: Emphasize project highlights, technical skills, and experience relevant to senior data infrastructure engineer roles.
Prepare for Technical Interviews: Brush up on stream processing, analytical databases, and distributed storage concepts, and practice data pipeline optimization and real-time streaming system design exercises.
Research Cybereason: Understand Cybereason's data infrastructure team, technology stack, and culture, focusing on customer obsession, technical excellence, and cutting-edge cybersecurity technology.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with Cybereason before making application decisions.

Senior Data Infrastructure Engineer