Job Title: Data Engineer – Airflow, dbt, Kubernetes/OpenShift

Location: Jersey City, NJ(Hybrid) / Need Local candidates only

Duration: 12 months

Eligibility: USC, GC, GC-EAD and H4-EAD

Job Description:

We are seeking a highly skilled Senior Data Engineer with 10+ 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

Job description