Python Kubernetes Software Engineer Data Jobs in North Carolina

Overview:

• Client is seeking a mid-level to senior engineer to join client’s team on a contract basis to help design, build, and operate a secure, scalable enterprise Data Private Cloud (DPC) platform.
• This is a hybrid role that blends container-platform development (Kubernetes/OpenShift and data services) with security operations (SecOps) and automation.
• Candidates will build and enhance platform services and workflows across the SDLC, implement security controls and compliance guardrails, and partner with cross-functional teams to operationalize secure-by-default data services at scale.

Key Responsibilities:
Platform Engineering:

• Design and build automated platform workflows for provisioning, deployment, and operational support of data services running on OpenShift/Kubernetes.
• Develop and maintain platform capabilities supporting data ecosystem components such as Spark, Iceberg, Ranger, Sparkflow, Superset, and related services.
• Contribute to resilient, scalable architecture for containerized workloads and large-scale data processing pipelines.
• Improve platform reliability through automation, runbooks, SRE practices, and standard operating procedures.

Security Engineering and SecOps Enablement

• Engineer security automation that enforces controls for data access, encryption, masking, and protection across the data platform.
• Integrate security into the SDLC by embedding controls into CI/CD pipelines, infrastructure-as-code, and release processes.
• Partner with security, platform, and DevOps teams to strengthen incident response readiness, operational resilience, and risk reduction.
• Support security monitoring and compliance by contributing to:
• Policy management, attestation evidence, and continuous compliance workflows
• Security-relevant audit logging, alerting, and dashboards

Hands-on Development and Collaboration:

• Design, implement, test, and document Python-based services and automation for platform operations and compliance workflows.
• Collaborate closely with architects, DevOps/platform engineers, and data product teams to deliver end-to-end solutions.
• Participate in technical design reviews, threat modeling discussions, and architecture decisions for secure deployment patterns.

Required Qualifications (5 plus years):
Core Skills:

• Strong Python development skills for enterprise-scale automation and service development.
• Solid understanding of security fundamentals (least privilege, defense-in-depth, secure SDLC) and common compliance concepts.
• Experience building or operating software in containerized environments (Kubernetes or OpenShift/OCP).
• Practical experience with CI/CD pipelines and integrating security checks/controls into delivery workflows.
• Strong communication skills and ability to work effectively across engineering and security stakeholders.

Technical Background:

• Familiarity with data security patterns such as access control, encryption, tokenization/masking, and secrets management.
• Understanding of DevOps practices: automated testing, release automation, environment promotion strategies, and operational support.
• Exposure to data platform concepts (data services, governance, metadata, batch/stream processing).

Preferred Qualifications (Nice to Have):

• Experience with Apache/open-source ecosystem tools such as Ranger, Keycloak, Spark, Iceberg, DataHub.
• Knowledge of S3-compatible object storage and large-scale distributed data processing patterns.
• Familiarity with observability tooling (logs/metrics/traces), security telemetry, and operational health dashboards.
• Experience with incident response, post-incident reviews, and improving operational resilience.
• Exposure to API design and/or UI development (e.g., React.js) for operational portals or admin tools.

What Success Looks Like:

• Automated workflows that make data services easy to deploy and operate on OCP/Kubernetes.
• Security controls that are built-in, not bolted-on—policy enforcement, least privilege, auditability, and compliance automation.
• Improved reliability and reduced operational overhead through standardization and automation.
• Strong cross-team alignment between platform engineering, data teams, and security stakeholders.