GCP Cloud Engineer

Charlotte, North Carolina, United StatesHybridContractPosted 2 months ago

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Role summary

This Lead Cloud Engineer role focuses on designing, deploying, and managing cloud-native platforms, primarily on Google Cloud Platform (GCP) and OpenShift, to support containerized Spark applications and statistical risk models. The engineer will architect and enhance platforms, drive modernization, and oversee large-scale data processing workflows. Key responsibilities include developing Python-based microservices, implementing robust CI/CD pipelines, ensuring system reliability through monitoring and troubleshooting, and adhering to security and compliance standards. The role involves cross-functional collaboration and technical mentorship, requiring strong leadership and deep expertise in distributed systems, cloud technologies, and big data processing.

Title: Software Engineer 4 (Lead Cloud Engineer)

Duration: 12 Months

Type: W2 Only

Location: Charlotte, NC – Hybrid Role

Key Responsibilities

Platform & Architecture Leadership

Lead design, deployment, and management of OpenShift clusters or GCP projects supporting containerized Spark applications
Architect and enhance the platform used for executing statistical risk models
Drive platform modernization and cloud adoption initiatives

Distributed Data Processing

Architect and oversee large-scale data processing workflows using Apache Spark
Optimize Spark jobs for performance and cost efficiency using Kubernetes orchestration and auto-scaling
Oversee integration of Spark with data sources (Kafka, S3/GCS, databases, data lakes)

Application & Tooling Development

Lead development of Python-based microservices and backend tools (Django preferred) to support statisticians and data users
Ensure scalability, usability, and reliability of tools and platform features

Automation & CI/CD

Design and govern CI/CD pipelines in GitHub Actions, Sonar, Helm, Harness, etc.
Implement automation across deployments, environment management, and testing

Monitoring & Reliability

Monitor and tune cluster health, performance, and resource allocation using Prometheus, Grafana, and GCP tools
Troubleshoot distributed and cloud-native system issues

Security & Compliance

Ensure solutions follow enterprise security requirements
Implement RBAC, encryption, and secure coding patterns

Cross-Functional Leadership

Partner with data scientists, platform engineering, DevOps, and architecture teams
Provide technical guidance and mentorship to junior and senior engineers
Lead production deployments and root-cause analysis efforts

Qualifications:

Experience

5–7+ years with Apache Spark for big data processing
3+ years with Python/Django backend development
3+ years building tools or platforms for high-volume data users
3–5+ years working with Google Cloud or equivalent cloud platforms
3+ years managing OpenShift/Kubernetes containerized workloads
Experience supporting data-heavy financial or regulated environments preferred

Technical Skills:

Proficiency in Spark frameworks (PySpark, Scala, or Java)
Strong experience with Docker/Kubernetes concepts
Hands-on OpenShift administration
Experience with conda environments
Strong coding in Python, Scala, or Java
CI/CD exposure: GitHub Actions, Helm, Harness, Sonar
Strong debugging skills across distributed systems
Knowledge of GCP services (GCS, IAM, GKE, Cloud Run, etc.)

Education

Bachelor’s degree in Computer Science, Engineering, or related discipline

Role Level

Lead Engineer: 8–10+ years with demonstrated leadership of complex platforms

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