This is a 6-month contract position for a Senior Data Engineer based in Jersey City, New Jersey. You will focus on designing, building, and optimizing enterprise-scale data pipelines that support financial and accounting platforms, with particular emphasis on workflow orchestration, data modeling, and cloud-native infrastructure. Responsibilities 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 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 Deploy and manage data workloads on Kubernetes and/or 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 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 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 Qualifications Required 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 (DAG design, scheduling, performance tuning) Expert-level dbt Core (data modeling, testing, macros, 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 on cloud environments Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows Preferred Experience supporting financial services or accounting platforms Exposure to enterprise system migrations (e.g., legacy platform to modern data stack) Experience with data warehouses such as Oracle