1 month ago
Seoul, Korea, SouthStaff+
Responsibilities
- Design and build security data lake and data warehouse architectures for detection engines, AI/ML models, and analysts.
- Design real-time security log collection architectures across Kubernetes, servers, cloud, and network environments.
- Build and operate ETL/ELT data pipelines and maintain reliability from log collection through data ingestion.
- Operate and enhance SIEM platforms, including detection-rule execution stability, indexing and search optimization, and new log-source onboarding.
- Design schemas and data models suitable for AI/ML use cases and build data enrichment pipelines.
- Build and operate detection-as-code frameworks for code-based detection-rule management, testing, and deployment.
- Optimize performance, query execution, reliability, and cost efficiency across large-scale SIEM and data warehouse platforms.
- Build monitoring and incident-response systems for data pipeline failures and improve data quality using AI/LLMs.
Requirements
- At least 7 years of experience in large-scale data engineering or backend system development.
- Experience building and operating large-scale data pipelines in AWS environments, including technologies such as Kafka, Kinesis, and S3.
- Proficiency in Python for data processing and backend development.
- Understanding of the structure and flow of security events and logs from diverse sources.
- Practical experience diagnosing data pipeline failures and ensuring system reliability.
- Experience designing and operating architectures in cloud infrastructure environments.
- Experience with Git-based collaboration and CI/CD pipeline operation.
- Preferred: experience building and operating large-scale platforms using cloud data warehouses.
- Preferred: experience operating data transformation and orchestration frameworks such as dbt and Airflow.
- Preferred: understanding or experience developing and using detection-as-code platforms.
- Preferred: experience managing infrastructure with IaC tools such as Terraform and CloudFormation.
- Preferred: practical experience with security data models such as OCSF and CIM.
- Preferred: experience building and operating SIEM solutions at scale.
- Preferred: experience using AI/LLMs for log normalization, schema mapping, and coverage analysis.
- Preferred: experience designing and operating security log collection architectures in Kubernetes.
Benefits
- Permanent employment with a 12-week probationary period; the probation period may be waived, shortened, or extended when required by the work.
- Work location is Coupang’s Seoul Guui Office.
Tech Stack
Categories
Data EngineeringSecurity
About Coupang
Coupang builds and operates a South Korea–focused e-commerce marketplace with an end-to-end logistics network (Rocket Delivery), plus food delivery, video streaming, and fintech under brands such as Coupang, Eats, and Play. Revenue comes from first-party retail, third-party marketplace services, advertising, and memberships (Rocket WOW). Founded in 2010, the company is headquartered in Seattle and is publicly listed on the NYSE (CPNG).