DataOps Engineer

Job summary

Company Description For More Open Positions Visit us at: http://recruiting.woongjininc.com/ Our Mission Woo***. is a rapidly growing team who provides a range of unique, exceptional, and enhanced services to our clients. We have a strong moral code that includes the service of goodness without expectations of reward.

Benefits

Qualifications

• Knowledge sharing: keep internal documentation up to date and run short tech demos or brown‑bag sessions on Iceberg, Docker best practices, and automation techniques.
• Bachelor’s degree in Computer Science, IT, Data Engineering, or a related field (Master’s a plus).
• 5+ years of hands‑on experience building and operating large‑scale data platforms (lake‑house, data‑warehouse, or big‑data ecosystems).
• Proven production experience with Apache Iceberg (table creation, partition management, schema evolution, catalog integration).
• Strong Docker
• Experience with cloud‑native data services on AWS, Azure, or GCP (EMR, Dataproc, Synapse, etc.).
• Familiarity with other lake‑house formats such as Delta Lake or Apache Hudi and ability to evaluate trade‑offs against Iceberg.
• Knowledge of streaming platforms (Kafka, Pulsar, Kinesis) and real‑time processing patterns.
• Relevant certifications (Databricks Lakehouse Associate, Google Professional Data Engineer, AWS Certified Data Analytics – Specialty, etc.).
• Background supporting data platforms in regulated industries (pharma, finance, healthcare) and understanding of associated compliance frameworks.
• Additional Information
• All your information will be kept confidential according to EEO guidelines.

Responsibilities

• We are motivated by the sense of responsibility and servant leadership.
• Iceberg operations: support tables, manage schema changes, partitions, snapshot retention, and keep the catalog (Hive Metastore, AWS Glue, Nessie, …) synchronized.
• Docker image creation & testing: write multi‑stage Dockerfiles for Spark/Flink/Presto, run local test environments with Docker‑Compose, and conduct vulnerability scans (Trivy, Snyk, …).
• Data pipeline development: build ETL/ELT jobs that ingest raw data and write to Iceberg tables; add simple streaming components using Kafka, Pulsar, or Kinesis when needed.
• CI/CD automation: configure pipelines (GitHub Actions, GitLab CI, Azure DevOps, …) to lint Dockerfiles, scan images, version Iceberg metadata, and deploy pipelines without downtime.
• Automation with Ansible/Python: script cluster provisioning, catalog configuration, vacuum/compaction, and other routine housekeeping tasks.
• Observability: instrument services with OpenTelemetry, Prometheus, Grafana, and Loki; create dashboards showing pipeline latency, resource usage, table health, and error rates; set up basic alerts.
• SLA monitoring: measure data freshness, job success rates, and query response times against agreed‑upon targets and report deviations.
• Incident response: join the on‑call rotation, perform first‑line diagnosis and resolution of pipeline failures, Iceberg metadata issues, or container crashes; write concise root‑cause analyses and suggest improvements.
• Security & compliance support: help enforce image signing, mTLS, IAM roles, and bucket policies; collaborate with the security team to meet GDPR, HIPAA, or ISO 27001

Skills

Certifications

Degrees

Work schedule

Industry

Company size

Security clearance