What are the responsibilities and job description for the Only W2 | DataOps Engineer position at PALNAR?
Job Role: DataOps Engineer
Location: Englewood Cliffs, NJ till September, 2026| October onward Plano, TX
Duration: Long Term Contract
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 Docker files 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 Docker files, 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 Open Telemetry, 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.
Minimum 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.