Netskope: Staff Engineer DevOps, Data Security (DLP) — Brazil

About Netskope

Netskope develops security and networking technology for cloud, data and AI environments. Its employer introduction identifies the Netskope One platform, a zero-trust engine and the NewEdge network as parts of that offering. The business describes giving organisations visibility and control while maintaining the performance of access to applications and information. This connects cybersecurity with networking: the products address both how digital resources are reached and how their use is controlled and protected.

The position and its setting

Netskope lists Staff Engineer DevOps, Data Security (DLP) in Brazil, under reference 8155629. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Security Services
• Employer listing date: 25 August 2026

Responsibilities and work described

• Please note, this team is hiring across multiple levels
• Candidates will be individually assessed and appropriately leveled based on their skills and experience
• The Netskope Data Security team is focused on providing unrivaled visibility and real-time data protection
• Our products help ensure that customers' data is protected wherever it lives
• Our engineers architect, build, test, deploy, and operate highly scalable, high-performance data protection services across public cloud, private cloud, and on-premises environments
• In this role, you will help build and operate the infrastructure, automation, and engineering platforms that enable our Data Security services to be deployed and operated reliably at global scale
• Work: closely with development, SRE, Platform Engineering, Operations, QA, and cloud infrastructure teams
• You can read more about our best-in-class Data Security solutions at Netskope One Data Security
• Deploy, operate, and troubleshoot Kubernetes-based services across public cloud, private cloud, and on-premises environments
• Building: and maintain automation for deployments, releases, configuration management, infrastructure, and operational workflows
• Development: tools and software that improve the reliability, scalability, and efficiency of our services and engineering processes
• Work: with development, SRE, Platform Engineering, Operations, QA, cloud providers, and other teams to resolve complex production and deployment issues
• Improvement: monitoring, metrics, logging, dashboards, and alerting to provide visibility into application and infrastructure health
• Investigate complex issues spanning applications, Kubernetes, networking, infrastructure, and cloud platforms, and drive them through root cause and resolution
• Identify repetitive or operationally expensive processes and replace them with reliable automation and tooling
• Help improve service scalability, performance, capacity management, and operational readiness

Essential requirements and candidate background

• 5+ years of experience in DevOps, Site Reliability Engineering, Platform Engineering, infrastructure engineering, or a related role
• Staff-level candidates will typically have 8+ years of relevant experience
• Experience: Linux and troubleshooting production systems
• Experience deploying and operating Kubernetes-based services in production
• Experience: at least one major public or private cloud platform such as AWS, GCP, Azure, or OpenStack
• Understanding: networking fundamentals including TCP/IP, DNS, load balancing, SSL/TLS, and cloud or Kubernetes networking
• Programming and automation experience using Python, Go, or a similar language
• Experience: CI/CD, infrastructure automation, or configuration management using tools such as Jenkins, Ansible, Terraform, or equivalent technologies
• Experience: production monitoring and observability using technologies such as Prometheus, Grafana, Sumo Logic, Elastic Stack, or equivalent platforms
• Troubleshooting and problem-solving skills across applications and infrastructure
• Independently own and deliver technical projects
• Written and verbal communication skills and the ability to collaborate effectively with geographically distributed teams
• Hands-on experience using AI-assisted development tools and coding agents to accelerate software development, automation, testing, debugging, and problem solving, with the ability to review, validate, and take ownership of AI-generated code

Preferred experience and additional strengths

• Experience building internal platforms, developer tooling, release automation, or other systems that improve engineering productivity
• Experience troubleshooting distributed systems and large-scale cloud services
• Software engineering practices, including code review, testing, debugging, and maintainability
• Experience working across multiple cloud or hybrid-cloud environments
• Additional Expectations for Staff-Level Candidates
• Staff-level candidates should demonstrate technical leadership beyond individual project execution
• This includes:
• Leading complex technical initiatives and driving solutions across multiple teams or systems
• Designing platforms, automation, or architectural improvements that have broader engineering impact
• Providing technical direction, mentoring engineers, and helping raise engineering standards across the organization
• Education
• BS in Computer Science, Engineering, or equivalent practical experience required

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Advertised location: Brazil

Benefits mentioned by the employer

• DevOps engineers who enjoy solving complex infrastructure and production engineering problems through a combination of software development, automation, and systems knowledge
• Have the opportunity to work across Kubernetes, cloud infrastructure, CI/CD, observability, networking, and distributed systems while developing tools and automation that improve how our engineering teams build, deploy, and operate services
• Also have opportunities to use modern AI-assisted development tools to accelerate automation, software development, troubleshooting, and engineering productivity

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Staff Engineer DevOps, Data Security (DLP) vacancy is not an offer for reference 8155629; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Netskope

Applications follow the official employer page linked below.

Open the official Netskope posting below and match the title and reference 8155629.

Compare your qualifications and experience with the essential conditions for the Brazil position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Kubernetes
• CI/CD
• AWS
• GCP
• Azure
• AI/ML
• Google Cloud
• Linux
• Terraform