Senior Data Engineer Airflow, DBT Core, Kubernetes/OpenShift

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