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)

This is a remote position.