Description

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

Key Skills

Education

Bachelor's degree