Location: Charlotte, NC
Salary: $69.00 USD Hourly - $74.00 USD Hourly
Description: Lead Cloud Engineer (Software Engineer)Focus: Apache Spark Google Cloud Platform Python MicroservicesLocation: Charlotte, NC - 300 S. Brevard Street
Schedule: Hybrid (3 days onsite per week)
Contract: 12-month contract with extension and conversion potential
Work Location & Eligibility- Required Location: Charlotte, NC (onsite at 300 S. Brevard Street)
- Hybrid Schedule: Minimum 3 days per week onsite
- Local Candidates Only: Candidates must already reside in the Charlotte region
- Note: Prior relocation attempts after offer acceptance have caused onboarding disruptions.
- Visa Status:
- H-1B transfers are discouraged due to historical start-date delays
- H-1B applicants will be considered, but not prioritized
Contract Details- Duration: 12 months
- Extensions: Possible based on performance, business need, and budget availability
- Conversion: Potential for full-time employment depending on performance and funding
Role OverviewThe Lead Cloud Engineer supports the enterprise
Model Risk Management (MRM) platform, which powers large-scale analytical and statistical workloads used for risk-based decisioning (e.g., credit approvals).
You will design, enhance, and operate a
Google Cloud Platform-based Spark ecosystem, enabling statisticians and data scientists to run complex models on massive datasets. This is one of the organization's first platforms fully migrated to Google Cloud Platform, offering the opportunity to lead modernization and cloud-native engineering initiatives.
Experience with large-scale data systems, distributed computing, or financial-services environments is valuable. Skills in AI/ML tooling or platform enablement are a strong plus.
Key ResponsibilitiesPlatform & Architecture Leadership- Lead design and operations of OpenShift clusters or Google Cloud Platform projects running containerized Spark workloads
- Architect enhancements for the statistical model execution platform
- Drive cloud modernization, automation, and infrastructure scalability initiatives
Distributed Data Processing- Architect and optimize Apache Spark data pipelines at enterprise scale
- Improve performance, reliability, and cost-efficiency using Kubernetes orchestration
- Integrate Spark with Kafka, S3S, data lakes, and enterprise databases
Application & Tooling Development- Lead development of Python microservices and backend applications (Django preferred)
- Build tools that support statisticians, data scientists, and model developers
- Ensure high reliability, scalability, and user-focused design
Automation & CI/CD- Design and govern CI/CD systems using GitHub Actions, Helm, Sonar, Harness, etc.
- Automate deployment, environment provisioning, monitoring, and testing
Monitoring & Reliability- Manage and improve cluster health using Prometheus, Grafana, and Google Cloud Platform observability tools
- Troubleshoot distributed systems and optimize resource allocations
Security & Compliance- Enforce enterprise security standards and regulatory requirements
- Implement RBAC, encryption, and secure development practices
Cross-Functional Leadership- Collaborate with data scientists, DevOps, architecture, and platform teams
- Mentor engineers across multiple experience levels
- Lead production releases, incident response, and root-cause analyses
QualificationsExperience- 5-7+ years using Apache Spark for large-scale data processing
- 3+ years developing Python/Django backend applications
- 3+ years building platforms or tools for high-volume data users
- 3-5+ years working with Google Cloud (or similar major cloud provider)
- 3+ years operating Kubernetes / OpenShift containerized workloads
- Experience in financial-services, risk, or regulated environments preferred
Technical Skills- Strong Spark (PySpark, Scala, or Java) expertise
- Deep understanding of Docker and Kubernetes architecture
- Hands-on OpenShift administration
- Experience with conda Python environments
- Strong coding skills in Python, Scala, or Java
- CI/CD experience (GitHub Actions, Helm, Sonar, Harness)
- Proficient in debugging distributed systems end-to-end
- Knowledge of Google Cloud Platform (GCS, IAM, GKE, Cloud Run, etc.)
Education- Bachelor's degree in Computer Science, Engineering, or related field
Role Level- Lead Engineer: 8-10+ years with demonstrated experience guiding complex technical platforms
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