Hiring Sr. Data Engineer
Abhinav Mohanty
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Role: Sr. Data Engineer
Location: Jersey City, NJ (Hybrid) – Need Local
NO H1B, OPT, CPT
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