Security Engineer, Veeam

• 5+ years in security engineering, cloud operations, or detection engineering, with recent hands-on ownership of production cloud security work
• Production Terraform experience. You've written and maintained modules other teams consume, and you know why a security control belongs in code rather than in a runbook
• Real operational depth in both Azure and AWS: identity models, logging and audit sources, network boundaries, and where each provider's defaults leave you exposed
• Demonstrated detection building. You've written detections against real telemetry, tuned them against real false positives, and can explain a specific rule you shipped and how you validated it
• Strong cloud security fundamentals: RBAC and least privilege, secret and key management, egress control, and public-exposure prevention
• Fluency in at least one query language for security data (KQL, SQL, or equivalent) and the judgment to know when a query problem is actually a data-model problem
• Scripting and automation ability in Python or a comparable language, enough to build and maintain tooling rather than only configure vendor products
• A track record of shipping production code or infrastructure you can point to. This is a hands-on building role, not an advisory one

Desirable

• We're looking for a Security Engineer III to own how we detect and respond to threats across our cloud estate, and to build the security data lake that makes that possible
• Our cloud-native SaaS platform runs on Microsoft Azure and AWS, delivering high-trust, secure data protection services to customers across regulated industries
• This role sits in Platform Security and owns the detection engineering and cloud security surface end to end: the pipelines that get security telemetry into one queryable place, the detections that fire off it, and the guardrails in Azure and AWS that stop the finding from recurring
• You'll partner closely with SRE, Product Engineering, and the rest of Security Engineering, and your work is based on shipped mechanisms rather than on advice given
• Design and build our security data lake: decide what telemetry we ingest from Azure, AWS, SaaS, and endpoint sources, how it's normalized and retained, and what it costs, so that detection engineers and incident responders can answer questions in minutes instead of days
• Write, tune, and version-control detections as code. Own the full lifecycle: hypothesis, query, test, deploy through CI/CD, measure false-positive rate, and retire what stops earning its keep
• Build the response side with the detections. Automate triage and enrichment, wire runbooks into the tooling, and cut mean-time-to-detect and mean-time-to-respond on the alert classes that matter most
• Hands on hardening our Azure and AWS environments: identity and RBAC boundaries, network egress, key and secret handling, logging coverage, and public-exposure control across multiple tenants and accounts
• Deliver cloud security controls as Terraform. Anything you fix once should land in the modules and pipelines so it stays fixed, in every environment, without a human remembering
• Turn cloud security posture findings into a prioritized, owned, and closing queue. Partner with the teams that own the resources and make remediation the path of least resistance
• Run detection coverage assessments against a recognized threat framework, find the gaps that matter for our platform and threat model, and close them
• Set direction on detection and cloud security tooling: what to adopt, what to retire, and what we build ourselves
• Technologies You’ll Work With
• Microsoft Sentinel, Log Analytics, and KQL for detection authoring and hunting
• Azure security services: Defender for Cloud, Entra ID, Key Vault, Azure Policy, Azure Monitor, Event Hubs
• AWS security services: CloudTrail, GuardDuty, Security Hub, IAM, Config, CloudWatch
• Terraform for all cloud security and detection infrastructure, with GitHub Actions and Azure DevOps as the delivery path
• Wiz for cloud security posture, attack-path analysis, and vulnerability signal
• Security data lake and pipeline components: object storage (ADLS / S3), OCSF-style normalization, Azure Data Explorer or an equivalent query engine, and Azure Functions / Lambda for glue
• Python for detection tooling, enrichment, and automation, with comfort reading PowerShell and Bash
• Sigma and detection-as-code patterns, plus Git-based review for every rule change