דרושים » ניהול ביניים » Senior DevOps Engineer- Posture

Requirements:
8-10 years experience with DevOps technologies.
Experience with design, build, development and maintenance of DevOps solutions.
Experience with one of the major cloud providers: AWS, GCP, Azure.
Experience Working cloud & on-prem environments and solutions.
Experienced with deployment technologies.
Experienced with CI\CD technologies.
Experience with bootstrapping projects, introducing new technologies and building systems from scratch.
Good coding capabilities (python\bash etc.)
Team Player!
Advantages:
Prior experience with endpoint security products (agents, sensors, collectors).
Background in working with AI components (training, inference, serving).
Tech Stack:
AWS, Kubernetes, EKS, ECS, Jenkins, IaC, GitHub, Terraform, Python, Ansible, Docker + Compose, ArgoCD, MongoDB, RabbitMQ, Redis, Go, Neo4J, AI, and more.

Requirements:
5+ years of hands-on DevOps experience building, shipping, and operating production systems
Infrastructure as Code: design and implement infrastructure automation using tools such as Terraform, Pulumi, or CloudFormation (modular code, reusable patterns, pipeline integration)
Cloud platforms: deep experience with AWS, GCP, or Azure (core services, networking, IAM)
Kubernetes: strong end-to-end understanding of Kubernetes as a system (routing/networking, scaling, security, observability, upgrades), with proven experience integrating data-centric components (e.g., Kafka, RDS, BigQuery, Aerospike).
GitOps & CI/CD: practical experience implementing pipelines and advanced delivery using tools such as Argo CD / Argo Rollouts, GitHub Actions, or similar

Observability: metrics, logs, and traces; actionable alerting and SLOs using tools such as Prometheus, Grafana, ELK/EFK, OpenTelemetry, or similar
Scalability & Performance: Proven experience managing production environments characterized by high traffic volumes and large amounts of data, with a focus on maintaining system reliability and cost-efficiency at scale.
You might also have
Coding proficiency in at least one language (e.g., Python or TypeScript); able to build production-grade automation and tools.
Data Pipeline Orchestration: Demonstrated success building and optimizing data pipeline deployment using modern tools (Airflow, Temporal, Kubernetes operators) and implementing GitOps practices for data workloads
Data Engineer Experience Focus: Track record of creating and improving self-service platforms, deployment tools, and monitoring solutions that measurably enhance data engineering team productivity
Data Infrastructure Deep Knowledge: Extensive experience designing infrastructure for data-intensive workloads including streaming platforms (Kafka, Kinesis), data processing frameworks (Spark, Flink), storage solutions, and comprehensive observability systems.

Requirements:
6+ years of DevOps or Infrastructure Engineering experience in high-growth product environments.
Proven track record of leading or owning complex system deployments and cloud architectures end-to-end.
Expertise in cloud platforms (preferably AWS) with strong understanding of networking, security, and scalability best practices.
Proficiency in scripting and automation using Python and Bash.
Hands-on experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
Deep familiarity with CI/CD systems (GitHub Actions, Jenkins, or similar) and container orchestration (Kubernetes, ECS, or EKS).
Experience building observability stacks (Prometheus, Grafana, ELK, Datadog, etc.).
Excellent collaboration and communication skills - a positive, pragmatic team player who thrives in high-velocity, tradeoff-driven environments.

Requirements:
BSc in Computer Science, Engineering, or equivalent experience
5+ years of experience as a DevOps Engineer or similar software engineering role
Proven experience with Docker and Kubernetes (EKS preferred)
Hands-on experience with monitoring and observability tools, including Prometheus, Grafana, Datadog, or similar.
Expertise in Terraform for AWS infrastructure-as-code deployments
Strong collaboration and interpersonal communication skills
Excellent analytical thinking and problem-solving mindset
Proficiency with relational databases
Solid knowledge of Python and Bash scripting
Experience with test automation - an advantage

Requirements:
6+ years of relevant DevOps experience in large-scale production environments
Extensive experience in cloud computing technologies, preferably AWS.
Experience with ELK stack and monitoring solution like Grafana/Prometheus
Proven experience with Infrastructure as Code (IaC) tools such as Terraform and Terragrunt.
Proficiency with GitOps and CI/CD methodologies and tools like ArgoCD, Crossplane
Proven experience working with Kubernetes (K8s) in production environments.
DevOps Security Mindset: Deep understanding of secure development lifecycles and infrastructure, with experience in building and managing the full application release cycle (CI/CD).
A solid understanding of computer networking fundamentals, k8s Ingress, and cloud security concepts.
Strong ability to lead, design, and execute cross-organization projects
Excellent communication skills and the ability to execute projects from design to implementation.
Experience with FEDRAMP -An advantage.

Requirements:
7+ years of experience as a DevOps Engineer, Platform Engineer, or in a similar infrastructure-focused role.
Strong hands-on expertise across the AWS Stack (e.g. EC2, EKS, RDS, VPC, IAM, S3, Lambda).
Mastery of Infrastructure as Code - Terraform or equivalent.
Deep operational knowledge of Kubernetes, including architecture, cluster management, networking, and advanced debugging in production environments.
Strong expertise in designing and managing CI/CD methodologies and platforms (e.g. Jenkins, Github Actions).
Experience with monitoring tools such as Prometheus, DataDog, Coralogix (OTEL), Grafana etc.
Proven prior experience building and maintaining highly-available, production-grade, and service-oriented systems.
Strong scripting and automation background in languages such as Python or Bash.
Exceptional communication and collaboration skills with the ability to articulate complex technical needs and influence cross-functional teams.
Strong knowledge of AWS Networking - an advantage.

Requirements:
5+ years of experience as a Full Stack Engineer in production environments
Strong backend development experience with Python, Node.js, Go, or similar modern backend technologies
Familiar with frontend experience with React, TypeScript, and modern frontend frameworks
Experience designing and consuming REST APIs, event-driven systems, and distributed services
Solid understanding of cloud-native architectures and scalable web applications
Experience working with or integrating AI systems, LLMs, or agent-based architectures.
Strong product mindset and ability to work closely with cross-functional teams
Excellent debugging, problem-solving, and communication skills
Nice to Have
Experience with agent orchestration, context management, or retrieval-augmented architectures
Experience with Kubernetes, Docker, Terraform, or modern DevOps practices
Background in cybersecurity, data-intensive systems, or highly regulated environments.

Requirements:
8+ years of experience in software development, system design, and architecture roles.
Strong hands-on experience with modern backend technologies such as Java and/or Python.
Practical experience with containerized and cloud-native systems (Docker, Kubernetes, AWS / Azure / GCP).
Experience designing and operating distributed systems, including messaging or streaming platforms (e.g., Kafka, RabbitMQ, Spark).
Solid understanding of distributed databases (and non-distributed) such as Postgres, Cassandra, SingleStore, Clikhouse or similar technologies.
Deep knowledge of engineering best practices: CI/CD, DevOps, testing, security, and agile development.
Proven experience working with production environments, including deployment pipelines, monitoring, and troubleshooting.
Strong problem-solving skills and the ability to balance ideal architecture with real-world constraints.
Natural technical leadership: able to influence, mentor, and collaborate without relying on authority.
Excellent communication skills and the ability to work effectively across teams.
B.Sc. or M.Sc. in Computer Science, Software Engineering, or equivalent practical experience.

Requirements:
5+ years of hands-on experience building scalable data infrastructure, particularly around data lake or data warehouse architectures.
Proven experience designing, building and operating production-grade systems and services.
Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and hands-on experience with modern data platforms and tools (e.g., Spark, Kafka, Airflow, dbt, open table formats, or similar).
Strong programming skills in Python and SQL.
Independent, proactive, and ownership-driven mindset.
Background in data platform engineering, backend engineering, DevOps, or DBA - strong advantage.
Experience with containerization technologies - advantage.

Requirements:
7+ years in a senior technical leadership role (e.g., Principal Architect, Staff/Lead Engineer). Demonstrated experience leading architecture in fast-moving environments, with a strong track record as a hands-on builder delivering production systems.
Proven experience architecting and operating large-scale, distributed, and data-intensive systems, with the ability to reason across end-to-end system flows, dependencies, and trade-offs. Experience in SaaS or cybersecurity domains is a strong advantage.
Deep, hands-on experience with AWS, GCP, or Azure, and strong expertise in Kubernetes and containerized environments. Ability to design for scale while maintaining simplicity, efficiency, and cost awareness.
Strong background in modern data platforms, streaming architectures, and complex event processing, including systems that handle large-scale, real-time data workloads.
Experience designing and building multi-tenant SaaS platforms with strong isolation and scalability characteristics. Familiarity with Bring Your Own Cloud or similar deployment models in enterprise environments.
Hands-on experience integrating GenAI capabilities into production systems, including RAG pipelines, agentic workflows, or LLM-based integrations.
Solid understanding of secure system design, cloud security principles, and enterprise compliance frameworks, with the ability to incorporate security into architectural decisions.