DataOps Engineer

Woongjin•Englewood Cliffs, NJ

7d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.