Data Platform Engineer

Job Title: Data Platform EngineerLocation: Jersey City, NJKey Responsibilities:Data Pipeline & OrchestrationDesign, develop, and maintain complex Airflow DAGs for batch and event-driven data pipelinesImplement best practices for DAG performance, dependency management, retries, SLA monitoring, and alertingOptimize Airflow scheduler, executor, and worker configurations for high-concurrency workloadsdbt Core & Data ModelingLead dbt Core implementation, including project structure, environments, and CI/CD integrationDesign and maintain robust dbt models (staging, intermediate, marts) following analytics engineering best practicesImplement dbt tests, documentation, macros, and incremental models to ensure data quality and performanceOptimize dbt query performance for large-scale datasets and downstream reporting needsCloud, Kubernetes & OpenShiftDeploy and manage data workloads on Kubernetes / OpenShift platformsDesign strategies for workload distribution, horizontal scaling, and resource optimizationConfigure CPU/memory requests and limits, autoscaling, and pod scheduling for data workloadsTroubleshoot container-level performance issues and resource contentionPerformance & ReliabilityMonitor and tune end-to-end pipeline performance across Airflow, dbt, and data platformsIdentify bottlenecks in query execution, orchestration, and infrastructureImplement observability solutions (logs, metrics, alerts) for proactive issue detectionEnsure high availability, fault tolerance, and resiliency of data pipelinesCollaboration & GovernanceWork closely with data architects, platform engineers, and business stakeholdersSupport financial reporting, accounting, and regulatory data use casesEnforce data engineering standards, security best practices, and governance policiesRequired Skills & Qualifications:Experience10+ years of professional experience in data engineering, analytics engineering, or platform engineering rolesProven experience designing and supporting enterprise-scale data platforms in production environmentsMust-Have Technical SkillsExpert-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 automationDeep understanding of Kubernetes and/or OpenShift in production environmentsExtensive experience with distributed workload management and performance optimizationStrong SQL skills for complex transformations and analyticsCloud & Platform ExperienceExperience running data platforms on cloud environmentsFamiliarity with containerized deployments, CI/CD pipelines, and Git-based workflowsPreferred QualificationsExperience supporting financial services or accounting platformsExposure to enterprise system migrations (e.g., legacy platform to modern data stack)Experience with data warehouses (Oracle)