Platform Data Engineer

Docker/Kubernetes Application Development and Kube Admin Skills (Incl. Helm)

Phyton Development is important

Observability Skills (Monitoring, and Logging ELK, Prometheus/Grafana preferably)

Airflow 2.7 + (Both as a user level who develops DAGs, and has knowledge of Airflow internals including customizations)

CICD (Azure DevOps)

Any experience with Control-M is good

Kubernetes:

- In depth mastery of core Kubernetes concepts, including Control Plane Architecture, Networking (CNI, Service, Ingress, NetworkPolicy), Storage (CSI, PV, PVC), Security (RBAC, Secrets), Scheduling, and Resource Management

- Must have proven track record of successfully deploying and managing large-scale, mission-critical Kubernetes clusters in production environment

Observability:

- Mastery of core observability principles, including Metrics, Logs, Traces, Baggage, SLOs/SLIs, Alerting, Instrumentation, and Correlation.

- Must have proven experience engineering/developing and implementing comprehensive observability solutions using the OpenTelemetry framework, resulting in significant reductions in Mean Time To Detection (MTTD) or Mean Time To Resolution (MTTR).

Frameworks: Opentelemetry or equivalent

Airflow:

- Strong understanding of core Airflow architecture, including Control-M integration, DAG best practices, scheduling, operators, and executors.

- Must have proven experience owning and stabilizing production Airflow environments, ensuring reliable execution of critical data workflows that support business operations.

Coding & Scripting: (Technologies)

- Proficiency in Python for automation, tooling, and systems development, coupled with strong Bash scripting skills.

- Demonstrated experience building impactful internal tools or services that significantly enhance developer productivity and platform capabilities.

- Demonstrated expertise in identifying and resolving highly complex, cross-functional issues within distributed systems.