Job Description
Who We Are
AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.
As a Staff Software Engineer, Infrastructure (Cloud), you will own the data infrastructure and cloud systems that power AeroVect’s autonomous driving platform. You’ll design and scale the pipelines that move operational data from real-world deployments into the systems that drive development, testing, and continuous improvement — ensuring reliability and performance
This is a highly impactful role at the intersection of cloud infrastructure and data engineering. You’ll have real ownership — designing systems from the ground up, not inheriting a finished stack. The opportunity to grow into a technical leadership role is real as the team scales.
You Will
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
You Have
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
We Prefer
Experience integrating on-prem and cloud data flows in a hybrid infrastructure environment
Background in data engineering for robotics, autonomous systems, or real-time operational data
Knowledge of monitoring, logging, and alerting tools such as Prometheus, Grafana, or the ELK Stack
Familiarity with networking, security, and distributed system performance optimization
Experience supporting safety-critical, real-time, or high-availability systems
