An analytics engineer models, tests, and maintains trusted analytics data for business and AI use. The role sits between data engineering and analytics: it turns raw warehouse tables into governed metrics, reusable joins, and validated business logic that dashboards and AI systems can rely on.
In cyber security, this matters because weak data foundations can hide fraud, distort detection, and make AI outputs untrustworthy. If a metric is defined differently across tools, attackers or careless changes can exploit the confusion to mask anomalies or inflate false confidence. Analytics engineers defend against that drift with version control, lineage, and validation pipelines, so changes to sensitive metrics are auditable and reproducible. In practice, they help security and AI teams answer a critical question: which numbers are safe to automate decisions from?