What does a Datadog developer do?
A Datadog developer writes code that extends the monitoring platform through custom Agent-based integrations and infrastructure as code. This role focuses on building Python classes that collect specific metrics from internal systems and sending that data to the observability pipeline. The developer also manages platform resources programmatically using application programming interfaces rather than manual console clicks. These actions create a unified view of system health across complex distributed environments.
- Builds custom Agent-based integrations by writing Python classes that implement the required check method. This code defines exactly which metrics the integration collects and how it formats the data for ingestion. The developer uses the ddev command-line tool to scaffold the initial project structure and manage the development lifecycle. This process ensures the new integration follows the standard directory layout and naming conventions required by the Datadog Agent.
- Authors configuration files such as conf.yaml to control how the Agent ingests data from the custom integration. These files sit in the conf.d directory and specify parameters like collection intervals and tag filters. The developer validates these settings against the platform documentation to prevent ingestion errors or data loss. Proper configuration allows operations teams to adjust monitoring behavior without modifying the underlying Python source code.
- Manages Datadog resources as code using the Terraform provider to interact with the platform APIs. This approach lets the developer define monitors, dashboards, and service level objectives in declarative configuration files. Changes to these resources go through version control systems before applying to the production environment. Using infrastructure as code prevents configuration drift and makes it easier to replicate setups across different staging or production accounts.
How to hire a Datadog developer on Upwork
Step 1: Post a job
Define your observability needs by specifying requirements for custom Agent integrations or infrastructure as code. The Job Post Generator powered by Umaโข, Upwork's Mindful AI drafts a complete post from a few sentences describing your goals. You can write a new post, update a saved draft, or reuse an existing post to start hiring.
- Request Python development skills for building Agent checks that collect specific metrics and send them to Datadog.
- Specify experience with the ddev tooling to scaffold integration structures and manage configuration files like conf.yaml.
- Include Terraform expertise if you need a freelancer to manage Datadog resources via APIs using the Datadog provider.
Step 2: Evaluate candidates
Look for portfolios that demonstrate working Agent-based integrations or Terraform modules for observability platforms. Uma runs instant video interviews and builds shortlists with side-by-side comparisons to help you assess technical fit quickly.
- Verify candidates have authored Python classes implementing the check method for custom data ingestion.
- Check for examples of conf.yaml files placed correctly in conf.d directories to control metric ingestion.
- Review Terraform code samples that create or edit monitors, dashboards, or other resources through Datadog APIs.
Step 3: Interview your top choices
Discuss specific troubleshooting scenarios involving Agent connectivity or configuration validation. Schedule these conversations within Upwork Messages to receive an immediate transcript and summary after each interview.
- Ask how they structure Python code to handle errors during metric collection without crashing the Agent.
- Question their process for testing integrations locally before deploying them to production environments.
- Explore their approach to managing state when using Terraform to update existing Datadog resources.
Step 4: Agree on scope and begin work
Set clear milestones for delivering scaffolded projects, working integration code, or infrastructure definitions. Use Upwork Messages and the contract workroom for communication while identity verification and Hourly Payment Protection secure your project funds.
- Define deliverables such as a fully functional Agent check that reports custom metrics to your dashboard.
- Establish acceptance criteria for conf.yaml configurations that correctly filter and tag incoming data streams.
- Outline Terraform tasks to provision specific monitors or alerts via the Datadog provider API.
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The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.