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)

Pay: $60.00 per hour

Work Location: In person

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