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Observability Datadog Jobs (NOW HIRING)

GDH

Senior Datadog Engineer

Almont, CO · On-site

$95 - $100/hr

Role Summary A Senior Datadog Engineer and architect responsible for designing, developing, and ... The position requires deep expertise in software development, cloud automation, observability ...

Datadog

Senior Product Manager - Serverless

Boston, MA · On-site

$187K - $240K/yr

You'll own the strategy and execution for Datadog's AWS Serverless observability offering, building experiences that provide visibility into distributed systems and simplify debugging and operations.

NoBS

Observability Engineer

Boston, MA · On-site

$57.25 - $78.50/hr

NoBS Tech is a rapidly growing Datadog Premier Partner specializing in advanced observability, monitoring, and cloud solutions for clients who value innovative, resilient, and data-driven operations.

Datadog

Distinguished Architect, AI

New York, NY · On-site

$300K - $484K/yr

Co-create custom observability integrations and solutions alongside Product SAs to keep Datadog at the absolute forefront of the AI stack. * Collaboration : Collaborate with Product Solutions ...

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Observability Datadog information

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How much do observability datadog jobs pay per hour?

As of Jun 29, 2026, the average hourly pay for observability datadog in the United States is $17.34, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $18.03 per hour, depending on experience, location, and employer.

Is it hard to get hired at Datadog?

Getting hired for an Observability Datadog role typically requires relevant technical skills such as experience with monitoring tools, cloud platforms, and scripting. The hiring process often involves technical interviews, coding assessments, and demonstrating knowledge of observability concepts, making it competitive but achievable with proper preparation.

Who is Datadog's biggest competitor?

For a role related to Observability at Datadog, its biggest competitors include companies like New Relic, Splunk, and Dynatrace, which offer similar monitoring and analytics solutions. These competitors provide cloud-based observability tools used by IT and DevOps teams to monitor infrastructure, applications, and performance metrics.

What are the key skills and qualifications needed to thrive as an Observability Engineer specializing in Datadog, and why are they important?

To excel as an Observability Engineer with a focus on Datadog, you need a strong background in IT operations, cloud infrastructure, and monitoring concepts, often supported by relevant degrees or certifications. Familiarity with Datadog's platform, scripting languages (like Python or Bash), and integrations with cloud services (AWS, Azure, GCP) is typically required. Analytical thinking, proactive problem-solving, and the ability to collaborate across teams are vital soft skills in this role. These skills ensure effective system monitoring, rapid incident response, and ongoing performance optimization in complex environments.

Does Datadog do observability?

Datadog is a platform that provides observability solutions, including monitoring, tracing, and logging for cloud infrastructure and applications. As an Observability Datadog professional, understanding these tools and how to implement them is essential for ensuring system performance and reliability.

What are Observability Datadog roles?

Observability Datadog roles typically refer to professionals who implement, manage, and optimize observability practices using the Datadog platform. These specialists focus on monitoring application performance, infrastructure health, and ensuring real-time visibility into system operations. They configure dashboards, set alerts, and analyze logs, traces, and metrics to detect and resolve issues quickly. Their work helps organizations maintain system reliability, optimize performance, and improve incident response.

How does an Observability Datadog specialist typically collaborate with development and operations teams?

An Observability Datadog specialist works closely with both development and operations teams to ensure that applications and infrastructure are properly monitored. They often participate in sprint planning and incident response meetings, helping teams define meaningful metrics, set up dashboards, and configure alerting policies. Collaboration also includes training team members on best practices for using Datadog and troubleshooting monitoring issues together. This cross-functional role ensures that all stakeholders have visibility into system health and can respond quickly to performance or reliability concerns.

What is the average salary at Datadog?

The average salary for an Observability Datadog role varies depending on experience and location but typically ranges from $100,000 to $140,000 annually. Salaries for related positions such as cloud engineers or monitoring specialists may also include bonuses and stock options. Candidates with skills in cloud monitoring, DevOps, and experience with Datadog tools tend to earn higher compensation.

What is the difference between Observability Datadog vs Cloud Engineer?

AspectObservability DatadogCloud Engineer
Primary FocusMonitoring, analytics, and visualization of system performanceDesigning, implementing, and managing cloud infrastructure
Required SkillsMonitoring tools, scripting, data analysisCloud platforms, scripting, infrastructure as code
CertificationsDatadog certifications, cloud provider certificationsAWS, Azure, or GCP certifications
Work EnvironmentIT operations, DevOps teamsCloud infrastructure teams, DevOps

While both roles involve cloud technologies, Observability Datadog specialists focus on monitoring and analyzing system performance, whereas Cloud Engineers design and maintain cloud infrastructure. Understanding these differences helps organizations assign the right skills to each role.

Infographic showing various Observability Datadog job openings in the United States as of June 2026, with employment types broken down into 97% Full Time, and 3% Contract. Highlights an 78% Physical, 6% Hybrid, and 16% Remote job distribution, with an average salary of $36,065 per year, or $17.3 per hour.
AWS Cloud Platform Engineer

AWS Cloud Platform Engineer

INGENworks

Los Angeles, CA • On-site

$60 - $80.25/hr

Other

Posted 5 days ago

Key responsibilities

  • Design and operate scalable, highly available, multi-account AWS cloud infrastructure.

  • Build and maintain Infrastructure-as-Code modules and standards using Terraform.

  • Develop reusable platform patterns, landing zones, and golden paths for engineering teams.

Job description

Job Title: AWS Cloud Platform Engineer

Duration: Long-term Contract

Location: LA, CA (Hybrid)

Overview

Senior Cloud Platform Engineer responsible for designing, building, and operating scalable, secure, and cost-efficient cloud infrastructure. Focus on platform enablement, infrastructure automation, CI/CD, event-driven streaming, and operational excellence across multi-account AWS environments — empowering engineering teams through self-service tooling and reliable, well-governed cloud foundations.

Key Responsibilities

  • Design and operate scalable, highly available, multi-account cloud infrastructure (AWS)
  • Build and maintain Infrastructure-as-Code modules and standards using Terraform
  • Develop reusable platform patterns, landing zones, and golden paths for engineering teams
  • Optimize and operate CI/CD pipelines (Jenkins, GitHub Actions, Harness)
  • Enable developer self-service and reduce manual intervention through automation
  • Manage Kubernetes platforms (EKS) — networking, scaling, upgrades, and workload onboarding
  • Operate and support Kafka-based event streaming platforms — topics, schemas, connectors, and cluster reliability
  • Build and integrate REST APIs and self-service tooling to streamline platform workflows
  • Implement cloud security and governance (IAM, OAuth/OIDC, OKTA, SSL/TLS, secrets management)
  • Drive cloud cost optimization, capacity planning, and FinOps practices
  • Implement observability — metrics, logging, tracing, alerting, and SLOs
  • Lead incident response, troubleshooting, and root cause analysis across platform and runtime systems
  • Partner with application teams to troubleshoot infrastructure, deployment, and runtime issues
  • Drive continuous improvement using operational insights and user feedback
  • Enhance documentation, runbooks, and platform usability

Technical Skills

  • Cloud: AWS (EKS, EC2, VPC/Networking, IAM, S3, RDS, Lambda)
  • IaC: Terraform (modules, state management, policy-as-code)
  • CI/CD: GitHub Actions, Harness
  • APIs & Integration: REST APIs (design, development, integration), Async APIs
  • Containers & Orchestration: Docker, Kubernetes (EKS)
  • Event Streaming: Kafka, Confluent (topics, schemas, Kafka Connect, cluster linking)
  • Monitoring/Observability: Datadog, CloudWatch
  • Security: OAuth, OIDC, OKTA, SSL/TLS, IAM, secrets management
  • Programming/Scripting: Java/Python

Key Competencies

  • Strong troubleshooting and problem-solving across distributed systems
  • Ability to translate operational issues into durable platform improvements
  • Systems-thinking approach to reliability, security, and cost
  • Effective collaboration and technical mentorship across engineering teams

Must Have & Desired Skills:

Must Have:

  • Cloud: AWS (EKS, EC2, VPC/Networking, IAM, S3, RDS, Lambda)
  • IaC: Terraform (modules, state management, policy-as-code)
  • CI/CD: GitHub Actions, Harness
  • APIs & Integration: REST APIs (design, development, integration), Async APIs
  • Containers & Orchestration: Docker, Kubernetes (EKS)
  • Security: OAuth, OIDC, OKTA, SSL/TLS, IAM, secrets management

Desired:

  • Event Streaming: Kafka, Confluent (topics, schemas, Kafka Connect, cluster linking)
  • Monitoring/Observability: Datadog, CloudWatch
  • Programming/Scripting: Java/Python

Apply