Description

You will build, operate, and optimize scalable data pipelines for financial and accounting platforms using Apache Airflow, dbt Core, and containerized cloud environments.

This role is on-site.

Responsibilities

• Design and maintain complex Airflow DAGs for batch and event-driven pipelines, ensuring high concurrency and SLA compliance.
• Lead dbt Core implementation, including project structure, CI/CD integration, and robust data modeling.
• Deploy and manage data workloads on Kubernetes/OpenShift, configuring resource limits, autoscaling, and pod scheduling.
• Optimize end-to-end pipeline performance by tuning Airflow executors, dbt queries, and container resource usage.
• Implement observability solutions for logs, metrics, and alerts to ensure pipeline reliability and fault tolerance.

Required Skills

• 8+ years of hands-on experience in enterprise data engineering or platform engineering.
• Expert-level proficiency in Apache Airflow, including DAG design, scheduling, and performance tuning.
• Deep expertise in dbt Core, covering data modeling, testing, macros, and incremental models.
• Strong proficiency in Python for data engineering automation and script development.
• Extensive experience with Kubernetes and/or OpenShift in production environments.
• Proven track record of designing enterprise-scale data platforms and managing distributed workloads.
• Strong SQL skills for complex transformations and analytics.
• Familiarity with CI/CD pipelines and Git-based workflows.

Preferred Skills

• Experience supporting financial services or accounting platforms.
• Exposure to enterprise system migrations from legacy platforms to modern data stacks.

Key Skills

Education

Any Graduate