Senior Data Engineer

About the role

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.

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.

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

Qualifications

• 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
• 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)