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# Data Engineer

- **Company:** ZIPSTAFF INC.
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Big Data, Health Informatics, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, OpenShift, Oracle (Applications), Performance Tuning, Cloud Services, Macros, Git, Kubernetes, Sql Tuning, Legacy Systems
- **Published:** September 17, 2026
- **Apply:** https://www.thejobnetwork.com/job/58a76b04-769f-4d77-9066-b685ab13ddaa/senior-data-engineer

## About the Role

* 10+ years in data engineering, analytics engineering, or data platform engineering.
 * Expert-level Apache Airflow (DAG design, scheduling, performance tuning).
 * Expert-level dbt Core (modeling, testing, macros, production implementation).
 * Strong Python for data engineering and automation.
 * Production Kubernetes and/or OpenShift for data workloads.
 * Strong SQL for complex transformations.
 * Experience with distributed workload management and performance tuning.
 * Cloud data platforms, containerized deploys, CI/CD, and Git.
 * Ability to work on-site in Jersey City, NJ.
 * Must be legally authorized to work in the United States without sponsorship now or in the future.

Preferred Qualifications:

 * Financial services or accounting data platforms.
 * Enterprise migrations from legacy systems to a modern data stack.
 * Data warehouse experience, including Oracle.

## Description

About the Opportunity:ZipStaff is seeking a Senior Data Engineer to build and operate enterprise data pipelines for a leading healthcare analytics organization supporting financial and accounting platforms.This role requires expert-level Apache Airflow, dbt Core, and production Kubernetes / OpenShift. General ETL or warehouse-only engineers without orchestration and container depth will not be a fit.
What You'll Do:

 * Design, build, and maintain complex Airflow DAGs (batch and event-driven).
 * Tune DAG performance, dependencies, retries, SLAs, and alerting.
 * Optimize Airflow scheduler, executor, and workers for high-concurrency workloads.
 * Lead dbt Core implementation: project structure, environments, and CI/CD.
 * Design staging / intermediate / mart models, tests, docs, macros, and incremental models.
 * Optimize dbt/SQL performance for large datasets and downstream reporting.
 * Deploy and run data workloads on Kubernetes / OpenShift.
 * Set CPU/memory requests and limits, autoscaling, and pod scheduling.
 * Troubleshoot container performance and resource contention.
 * Monitor end-to-end pipeline health; add logs, metrics, and alerts.
 * Partner with architects, platform engineers, and business stakeholders.
 * Support financial reporting, accounting, and regulated data use cases.
 * Enforce data-engineering standards, security, and governance.


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