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)
Required Skills :
Basic Qualification :
Additional Skills :
This is a high PRIORITY requisition. This is a PROACTIVE requisition
Background Check : No
Drug Screen : No
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 cri...
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)
Required Skills :
Basic Qualification :
Additional Skills :
This is a high PRIORITY requisition. This is a PROACTIVE requisition
Background Check : No
Drug Screen : No
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