Data Platform Engineer

Data Platform Engineer

Jersey City

Rate: $70/hr. C2C

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: 2 rounds, 1 virtual and 1 in person
\*When submitting please make sure resume is 3 pages or less, this is required by BBH; also please include their 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