Must have:
-Python
-Apache Airflow/DBT
-Communication, both written & verbal
-Kubernetes
-OpenShift
-8+ years of experience
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
Required Skills & Qualifications:
Experience
- 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
Must-Have Technical Skills
- 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
Cloud & Platform Experience
- Experience running data platforms on cloud environments
- Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows
Preferred Qualifications
- Experience supporting financial services or accounting platforms
- Exposure to enterprise system migrations (e.g., legacy platform to modern data stack)
- Experience with data warehouses (Oracle)
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