Data Platform Engineer

Data Platform Engineer

Jersey City, NJ 07311

12+ Months

USC, GC, GC-EAD and H4-EAD

Location: Onsite 3 days/week in Jersey City, NJ (185 Hudson St #1150, Jersey City, NJ 07311); no relocation

Start: ASAP

Interview Process: in-person

\*When submitting please make sure resume is 3 pages or less; also please include your LinkedIn profile with picture and full name \*

Must have
:

-Apache Airflow/DBT

-Kubernetes

-OpenShift

-Python

-Communication, both written & verbal

-8+ years of experience

Job description:

We are seeking a highly skilled
Senior Data Engineer with 8+ years of hands-on experience
in enterprise data engineering, including deep expertise in Apache Airflow DAG development, dbt Core modeling and implementation, and cloud-native container platforms (Kubernetes / OpenShift).

This role is critical to building, operating, and optimizing scalable data pipelines that support financial and accounting platforms, including enterprise system migrations and high-volume data processing workloads.

The ideal candidate will have extensive hands-on experience in workflow orchestration, data modeling, performance tuning, and distributed workload management in containerized environments.

**Key Responsibilities
:**

*Data Pipeline & Orchestration*

• Design, develop, and maintain complex Airflow DAGs for batch and event-driven data pipelines
• Implement best practices for DAG performance, dependency management, retries, SLA monitoring, and alerting
• Optimize Airflow scheduler, executor, and worker configurations for high-concurrency workloads

*dbt Core & Data Modeling*

• Lead dbt Core implementation, including project structure, environments, and CI/CD integration
• Design and maintain robust dbt models (staging, intermediate, marts) following analytics engineering best practices
• Implement dbt tests, documentation, macros, and incremental models to ensure data quality and performance
• Optimize dbt query performance for large-scale datasets and downstream reporting needs

*Cloud, Kubernetes & OpenShift*

• Deploy and manage data workloads on Kubernetes / OpenShift platforms
• Design strategies for workload distribution, horizontal scaling, and resource optimization
• Configure CPU/memory requests and limits, autoscaling, and pod scheduling for data workloads
• Troubleshoot container-level performance issues and resource contention

*Performance & Reliability*

• Monitor and tune end-to-end pipeline performance across Airflow, dbt, and data platforms
• Identify bottlenecks in query execution, orchestration, and infrastructure
• Implement observability solutions (logs, metrics, alerts) for proactive issue detection
• Ensure high availability, fault tolerance, and resiliency of data pipelines

*Collaboration & Governance*

• Work closely with data architects, platform engineers, and business stakeholders
• Support financial reporting, accounting, and regulatory data use cases
• Enforce data engineering standards, security best practices, and governance policies