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Requirements:
Core Data Engineering
3+ years of experience as a Data Engineer
Strong hands-on experience with Google Cloud Platform
Advanced SQL (BigQuery performance, partitioning, clustering)
Strong Python (data pipelines, APIs, batch & streaming jobs)
Experience building batch and streaming pipelines
BigQuery
Dataflow / Apache Beam
Pub/Sub
Cloud Storage
Data modeling for analytics and ML use cases
Production troubleshooting, optimization, and cost awareness

ML & GenAI
Experience supporting ML pipelines (training, inference, feature prep)
Hands-on with at least one of:
Vertex AI
Feature stores
Model inference pipelines
Experience working with LLM-based systems, including:
Embeddings generation
Vector databases / similarity search
RAG-style pipelines
Ability to prepare, version, and serve data for GenAI / ML workloads

Platform & Engineering
Experience with Airflow / Cloud Composer or equivalent orchestration
CI/CD pipelines for data & ML workloads
Infrastructure-as-code familiarity (Terraform preferred)
Understanding of IAM, data security, and access controls on GCP

Nice to Have
Experience with FinOps / cost optimization
Experience with real-time ML or low-latency inference
Experience with monitoring & observability for data/ML pipelines
Google Cloud certifications (Data Engineer / Architect)

Requirements:
Core Security & Operations
3+ years experience in SecOps / Cloud Security / Security Engineering
Hands-on experience securing Google Cloud Platform environments
Strong understanding of:
IAM (roles, service accounts, workload identity)
Network security (VPCs, firewall rules, load balancers)
Organization policies and security baselines
Experience operating and tuning security controls in production

Detection, Monitoring & Response
Experience with Google Cloud Security Command Center
Experience with Google SecOps (Chronicle) or equivalent SIEM
Building and tuning:
Detection rules
Alerts
Dashboards
Incident triage, investigation, and response in cloud environments
Log ingestion and normalization from cloud and third-party sources

Automation & Engineering
Strong scripting skills (Python preferred)
Security automation (SOAR-like workflows, custom tooling)
Experience integrating security tooling via APIs
CI/CD security controls (shift-left, pipeline security checks)
Cloud & Platform Security

Experience securing:
GKE
Cloud Run
Compute Engine
Knowledge of container security concepts (images, registries, runtime)
Vulnerability management and remediation workflows

Governance & Compliance
Experience with security posture management
Familiarity with common frameworks (ISO 27001, SOC 2, CIS Benchmarks)
Risk assessment and security findings remediation

Nice to Have:
Experience with Wiz, Prisma Cloud, or similar CSPM tools
Experience with threat modeling and attack simulations
Experience working with regulated environments
Google Cloud security certifications

Requirements:
Proven experience as a Cloud Architect, Cloud Solutions Architect, or in a similar cloud infrastructure role.
Deep understanding of cloud computing principles, microservices architectures, and serverless computing.
Expertise in at least one major cloud provider (GCP, AWS, Azure) and their core services (compute, storage, networking, databases).
Strong knowledge of containerization platforms and orchestration tools (Docker, Kubernetes).
Extensive experience with Infrastructure as Code tools (e.g., Terraform, CloudFormation, Pulumi) and scripting languages (Python, Bash).
Solid understanding of cloud networking (VPC, VPN, routing, load balancing) and cloud security concepts.
Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
Strong problem-solving, analytical skills, and a customer-centric mindset.
Bachelors degree in Computer Science, Engineering, or a related field.
Advantage: Professional-level cloud certifications (e.g., Google Cloud Professional Cloud Architect, AWS Certified Solutions Architect - Professional).

Requirements:
Requirements & Qualifications:

Leadership Experience: 2+ years of experience leading technical teams (architects, DevOps, or engineering squads), including mentoring, task prioritization, and delivery accountability - ideally in a professional services or consulting environment.

Communication & Presentation: Outstanding verbal and written communication skills in English & Hebrew. Proven ability to conduct discovery calls and articulate sophisticated technical concepts to both engineering teams and non-technical business executives, and to represent the company at a senior level.

Cloud Platform Expertise: 5+ years of proven experience architecting, deploying, and managing complex production workloads on Google Cloud Platform (GCP) and/or Amazon Web Services (AWS), with deep knowledge of their core services (compute, storage, networking, databases).

Core Technical Foundations: Strong foundational knowledge of operating systems (Linux/Windows), virtualization environments, database architectures (SQL/NoSQL), and proficiency in scripting languages (such as Python and Bash) for automation and troubleshooting.

Modernization & Serverless Mastery: Practical experience implementing containerized solutions (Docker, Kubernetes/GKE/EKS) and serverless technologies (Cloud Run, AWS Fargate, ECS, Lambda, Cloud Run Functions).

Hands-On DevOps & IaC: Strong, hands-on background in DevOps practices. Expert-level knowledge of Infrastructure as Code (IaC) tools - specifically Terraform - along with deep experience in CI/CD pipeline construction.

Migration Experience: Proven track record of guiding enterprise migrations (re-hosting, re-platforming, and refactoring), particularly On-Prem to Cloud or cloud-to-cloud migrations (such as AWS to GCP).

Client-Facing Professionalism: A consultative, customer-centric mindset with strong analytical, scoping, and problem-solving abilities, and the maturity to own decisions independently under broad executive guidance.

AI/ML Familiarity: An understanding of the modern AI landscape, with the ability to identify opportunities where Generative AI can drive value for clients.

Education: Bachelor degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Preferred Advantages
Multi-Cloud experience: Hands-on architectural and deployment experience with both GCP and AWS.
Prior experience as a Team Leader, Delivery Lead, or Principal Architect in a cloud consulting / MSP / professional services company.
One of the following Professional-level cloud certifications:
- Google Cloud Professional Cloud Architect
- AWS Certified Solutions Architect - Professional
Deep expertise in FinOps methodologies and cloud cost allocation tools.