DataOps Engineer

Woongjin, Inc$110K — $130K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, IT, Data Engineering, or related field (Master's a plus).
  • 5+ years of hands-on experience with large-scale data platforms (lake-house, data-warehouse, big-data ecosystems).
  • Production experience with Apache Iceberg (table creation, partition management, schema evolution).
  • Strong Docker skills for multi-stage builds and security scanning.
  • Experience with major data-processing engines (Spark, Flink, Presto/Trino) linked to Iceberg tables.
  • Proficiency in Python and/or Ansible for automation tasks.
  • Experience with CI/CD pipelines for Docker and data pipeline code deployment.
  • Familiarity with observability tools (Prometheus, Grafana) for monitoring.

Responsibilities

  • Support Iceberg operations: manage tables, schema changes, and ensure catalog synchronization.
  • Create and test Docker images: write multi-stage Dockerfiles and run local test environments.
  • Develop data pipelines: build ETL/ELT jobs and implement streaming components as needed.
  • Configure CI/CD automation: set up pipelines for Dockerfile linting, image scanning, and metadata versioning.
  • Automate tasks with Ansible/Python: script for provisioning, configuration, and housekeeping.
  • Implement observability: use tools like OpenTelemetry to create dashboards and alerts for pipeline health.
  • Monitor service level agreements: measure data freshness and job success rates against targets and report issues.
  • Respond to incidents: be on-call to diagnose and resolve data pipeline failures and perform root-cause analyses.

Benefits

  • Medical Insurance
  • Vision Insurance
  • Dental Insurance
  • 401(k)
  • Paid Sick Hours
Full Job Description


Benefits
  • Medical Insurance
  • Vision Insurance
  • Dental Insurance
  • 401(k)
  • Paid Sick hours


Job Description

We are looking for a mid-level engineer to build and operate a data platform that uses Apache Iceberg as the lake-house table format and Docker-based micro-services (Spark, Flink, Presto, etc.). you will own the end-to-end delivery pipeline, monitoring, security, and incident response, ensuring the platform runs reliably at scale.

Key Responsibilities
  • 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 requirements.
  • 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.


Qualifications
  • 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 skills: multi-stage builds, Docker-Compose testing, routine image security scanning.
  • Experience with at least one major data-processing engine (Spark, Flink, or Presto/Trino) and its connection to Iceberg tables.
  • Proficiency in Python and/or Ansible for automating infrastructure and platform tasks.
  • Experience building CI/CD pipelines that include Docker linting, vulnerability scanning, and automated deployment of data-pipeline code.
  • Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry, Loki) and ability to create useful alerts and dashboards.
  • Ability to respond to incidents, write clear root-cause analysis reports, and contribute to post-mortem actions.
  • Willingness to participate in an on-call rotation as a first-line responder.
  • Availability to work on-site in New Jersey for the initial assignment and relocate to Dallas by October 2026.


Preferred Qualifications
  • 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

*** NO C2C ***

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