Data Engineer

Title: Data Engineer Airflow, dbt, Kubernetes/OpenShift

Location: Jersey City, NJ (Onsite)

Duration: 12 Months Contract

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