Senior Data Engineer Airflow, DBT Core, Kubernetes/OpenShift
About The Position
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.
Requirements
- 8+ years of hands-on experience in enterprise data engineering.
- Deep expertise in Apache Airflow DAG development.
- Deep expertise in dbt Core modeling and implementation.
- Deep expertise in cloud-native container platforms (Kubernetes / OpenShift).
- Extensive hands-on experience in workflow orchestration.
- Extensive hands-on experience in data modeling.
- Extensive hands-on experience in performance tuning.
- Extensive hands-on experience in distributed workload management in containerized environments.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
