Data Platform Engineer - Jersey City
About The Position
The Data Platform Engineer will build, operate, and optimize scalable data pipelines supporting financial and accounting platforms, enterprise system migrations, and high-volume data processing. The role requires hands-on expertise in Apache Airflow DAG development, dbt Core modeling and implementation, and cloud-native container platforms such as Kubernetes and OpenShift. The engineer will develop and maintain batch and event-driven workflows, lead dbt implementation and modeling practices, and deploy data workloads in containerized environments. The position also focuses on performance tuning, distributed workload management, observability, pipeline reliability, and collaboration with data architects, platform engineers, and business stakeholders. The engineer will support financial reporting, accounting, and regulatory data use cases while following security, governance, and data engineering standards.
Requirements
- 10+ years of professional experience in data engineering, analytics engineering, or platform engineering roles.
- Proven experience designing and supporting enterprise-scale data platforms in production environments.
- Expert-level Apache Airflow expertise, including DAG design, scheduling, and performance tuning.
- Expert-level dbt Core expertise, including data modeling, testing, macros, and implementation.
- Strong proficiency in Python for data engineering and automation.
- Deep understanding of Kubernetes and/or OpenShift in production environments.
- Extensive experience with distributed workload management and performance optimization.
- Strong SQL skills for complex transformations and analytics.
- Experience running data platforms in cloud environments.
- Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows.
Nice To Haves
- Experience supporting financial services or accounting platforms.
- Exposure to enterprise system migrations, such as moving from a legacy platform to a modern data stack.
- Experience with data warehouses, including Oracle.
Responsibilities
- Design, develop, and maintain complex Airflow DAGs for batch and event-driven data pipelines.
- Apply best practices for DAG performance, dependency management, retries, SLA monitoring, and alerting.
- Optimize Airflow scheduler, executor, and worker configurations for high-concurrency workloads.
- Lead dbt Core implementation, including project structure, environments, and CI/CD integration.
- Design and maintain staging, intermediate, and mart dbt models following analytics engineering best practices.
- Implement dbt tests, documentation, macros, and incremental models to support data quality and performance.
- Optimize dbt query performance for large-scale datasets and downstream reporting.
- Deploy and manage data workloads on Kubernetes and/or OpenShift platforms.
- Design workload distribution, horizontal scaling, and resource optimization strategies.
- Configure CPU and memory requests and limits, autoscaling, and pod scheduling for data workloads.
- Troubleshoot container-level performance issues and resource contention.
- 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, including logs, metrics, and alerts, for proactive issue detection.
- Ensure high availability, fault tolerance, and resiliency of data pipelines.
- Collaborate 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
