Staff Software Engineer, Infrastructure (Cloud)
New
A
AeroVectAutonomous Ground Handling
Remote; Secondary Locations: New York City - Hybrid, Italy , Poland, Stockholm, London , Berlin , Amsterdam, Paris , Toronto - Remote, Seattle, Florida - Remote, Frankfurt , United Kingdom (Remote), Colorado, Vancouver - Remote, Boston - Remote, Munich, Atlanta - Hybrid, South San Francisco - Hybrid, Texas - RemoteFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSDockerPythonKafkaKubernetesData engineeringgRPCCI/CDTerraformCloudFormation
Requirements
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field
- 7+ years of experience building cloud infrastructure and data systems for large-scale distributed systems
- Strong proficiency in Python with a focus on data pipeline development and automation
- Hands-on experience with Kafka, Kubernetes, and gRPC for building scalable, high-throughput data systems
- Deep expertise with AWS cloud services including compute, storage, and data processing
- Experience with CI/CD platforms (Jenkins, GitHub Actions, CircleCI)
- Proficiency with containerization and orchestration (Docker, Kubernetes)
- Experience automating deployments with Terraform or CloudFormation
- Familiarity with Git-based workflows, code review processes, and collaborative software development
Responsibilities
- Design, build, and maintain scalable data pipelines that move operational data from on-prem systems into cloud infrastructure
- Architect and manage cloud-based infrastructure (AWS) for scalable computation, data processing, and system telemetry
- Integrate on-prem and cloud data flows into a cohesive, reliable data architecture
- Build and maintain CI/CD pipelines to enable rapid, reliable software delivery and validation across simulation and real-world testing environments
- Implement and manage infrastructure-as-code (Terraform, CloudFormation) for consistent, automated provisioning
- Develop internal services and automation tools to streamline data ingestion, processing, and observability
- Establish and enforce best practices for data reliability, pipeline observability, and system security
- Drive root-cause analysis for data infrastructure and pipeline issues; design long-term solutions to improve system resilience
- Collaborate with autonomy and systems engineers to define scalable data interfaces and cloud deployment strategies
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