Senior Datadog Security & Observability Engineer

Description

Keeper is hiring a talented Senior DataDog Engineer to join our DevOps team. This can be a 100% remote position from select locations with an opportunity to work a hybrid schedule for candidates based in the El Dorado Hills, CA or Chicago, IL metro area.

Keeper’s cybersecurity software is trusted by millions of people and thousands of organizations globally. Keeper is published in 23 languages and sold in over 150 countries. Join one of the fastest-growing cybersecurity companies and help scale the Datadog platform that supports Keeper’s security visibility, detection maturity and operational readiness.

About Keeper

Keeper Security is one of the fastest-growing cybersecurity software companies that protects thousands of organizations and millions of people in over 150 countries. Keeper is a pioneer of zero-knowledge and zero-trust security built for any IT environment. Its core offering, KeeperPAM®, is an AI-enabled, cloud-native platform that protects all users, devices and infrastructure from cyber attacks. Recognized for its innovation in the Gartner Magic Quadrant for Privileged Access Management (PAM), Keeper secures passwords and passkeys, infrastructure secrets, remote connections and endpoints with role-based enforcement policies, least privilege and just-in-time access. Learn why Keeper is trusted by leading organizations to defend against modern adversaries at KeeperSecurity.com .

About the Job

This is a Datadog-first security engineering role focused on detection engineering, SIEM operations and security observability across Keeper’s production and corporate environments. The ideal candidate has deep, hands-on experience administering and scaling Datadog, including Cloud SIEM, log management, security monitoring, dashboards, monitors and telemetry pipelines. Reporting to the Director of IT and Security, this role will partner closely with Security Operations, Infrastructure, SRE and Engineering teams to ensure logs, metrics, traces and endpoint signals are actionable, scalable and aligned to real-world threat scenarios. General SIEM experience without substantial production experience in Datadog will not be sufficient for this role.

Responsibilities

Own and continuously improve Datadog Cloud SIEM, security monitoring and observability capabilities across production and corporate environments

Design, build and maintain detection and telemetry capabilities across Datadog, SentinelOne, Wiz and related security platforms

Develop, test and tune high-fidelity Datadog detection rules aligned to real-world attack scenarios and adversary behaviors

Improve alert quality by reducing false positives, eliminating noise and increasing detection accuracy

Design and maintain Datadog log pipelines, processors, parsing rules, facets, indexes, archives and retention strategies

Implement and mature detection-as-code practices for scalable, version-controlled and testable rule management

Define and enforce logging, telemetry and instrumentation standards across cloud infrastructure, applications, endpoints and identity systems

Build and optimize log ingestion, parsing, normalization, enrichment and routing workflows

Automate onboarding of new telemetry sources and improve visibility across production and corporate environments

Correlate signals across Datadog, EDR, cloud, identity and security platforms to improve detection depth and investigation quality

Partner with Security Operations to improve triage workflows, incident response readiness and escalation quality

Build Datadog dashboards, monitors, analytics and reporting that support operational decision-making across Security, SRE and Engineering

Map and maintain detection coverage against MITRE ATT&CK and identify telemetry and detection gaps

Perform detection gap assessments and evolve use cases based on threat intelligence, threat hunting and emerging risks

Collaborate with cloud, infrastructure, product and compliance teams to strengthen secure logging and observability patterns throughout the software development lifecycle

Use AI-assisted tools such as Claude, ChatGPT or similar platforms to support query development, detection engineering, investigations, automation and technical documentation