Observability engineer

The observability engineer designs and operates a shared telemetry platform to help technical teams understand and diagnose their systems. They work on signal generation, collection, storage and access. Their deliverable is an operational data pipeline that developers and the teams responsible for services can use.

Metrics describe measurements taken during execution. Logs record events. Traces follow a request across services. Profiles show resource consumption at code level. These signals provide different insights. Context propagation can link them across several services, but cannot explain every cause of a malfunction on its own.

The work may include instrumentation libraries, SDKs and collector configuration. The engineer then organises the processing and routing of data to systems that allow it to be stored and queried. Continuous profiling can complement this work. Its importance depends on the role: it is an explicit responsibility at Anthropic and desirable experience at Adyen.

The reporting line should reflect the responsibilities assigned. To define the role, specify which team owns the platform, who uses it and who decides on its architecture. Distinguish delivering integrations under technical direction from defining a shared strategy for several teams. The job title alone does not describe this autonomy.

Observability engineer, SRE and performance engineer: who does what?

• The observability engineer builds and develops the shared telemetry platform.
• The SRE is primarily responsible for service reliability, with monitoring and operational activities.
• The performance engineer focuses on optimising system behaviour.

These responsibilities can overlap. A role may combine observability, optimisation and on-call duties. Specify the services concerned and production responsibilities before looking for a candidate.

This hire shapes how teams access data from their systems. A platform may receive signals without answering its users’ questions. The need then concerns instrumentation coverage, data processing and diagnostic interfaces. Defining these areas helps distinguish integration work from a broader architecture project.

One initial challenge is to link useful signals. It must be possible to correlate a trace with logs from several services when diagnosis requires it. Instrumentation and context propagation are among the mechanisms to examine. Asking only for new dashboards can overlook the quality of the data that feeds them.

Volume is another trade-off. Collecting, retaining and querying signals uses resources. On a large-scale platform, high cardinality, sampling, retention and storage limits call for specific technical choices. The person responsible for recruitment should clarify the uses to preserve and the cost and reliability constraints the candidate will need to address.

Adoption also needs an explicit owner. Application teams need to understand how to instrument their services and use the resulting data. The roles examined combine platform construction, understanding needs and simplifying tools. A candidate who can explain trade-offs can help users choose the signals relevant to their work.

Fictional example: several services produce logs and traces, but their developers do not know how to correlate them to follow a request. The work to assign may begin with instrumentation and context propagation. Replacing the visualisation system is then only one option to consider after this diagnosis.

Finally, align the autonomy required with the decisions expected. Delivering an integration within a defined architecture and leading a platform strategy for several teams involve different responsibilities. Poorly defining the role may lead to hiring someone competent in one part of the pipeline when the organisation expects them to take responsibility for the whole of it.

• Build and develop libraries or SDKs that produce signals suited to application teams’ needs.
• Configure collectors and pipelines for processing and exporting telemetry data.
• Design the pathways for storing and accessing operational data.
• Link signals across several services to make system diagnosis easier.
• Make trade-offs between collection, retention and querying according to volumes, uses, and cost and reliability constraints.
• Integrate continuous profiling when analysing resource consumption is part of the role.
• Support user teams in instrumenting their applications and using the platform.
• Explain technical trade-offs and simplify the tools teams use.

Useful knowledge combines software development, distributed systems, instrumentation and operational data processing. To assess a candidate’s background, look for work that shows how they have produced signals, built an integration or developed a platform. Experience with SDKs provides a concrete reference point when the role involves instrumentation libraries.

Experience should match the responsibilities to be assigned. One candidate may have delivered components within an architecture chosen by another engineer. Another may have defined collection and storage choices, then supported several user teams. These experiences do not demonstrate the same autonomy. Examine what each candidate has personally designed, operated and explained to users.

The programming language should relate to the work expected. EPAM cites Python for its telemetry extensions. Adyen mentions Go, Java or Python. These examples help to discuss the ability to develop integrations, without turning several languages into cumulative prerequisites. Understanding the pipeline remains useful beyond the language already used.

Educational backgrounds vary across the vacancies examined. Anthropic accepts a bachelor’s degree or an equivalent combination of education, training and experience. PepsiCo requires a bachelor’s or master’s degree in a technical discipline. These requirements describe specific recruitment processes and do not establish a requirement for a French qualification.

To define your need, therefore, relate the candidate’s knowledge to the components and activities of the role: SDKs, collectors, storage, queries and how teams use them. Explore profiling experience in more depth if you expect analysis of resource consumption at code level. The assessment should then distinguish claimed knowledge from decisions the candidate can explain.

To assess this profile, relate each skill to the role’s responsibilities and the decisions the candidate will be able to make.

1. Define the criteria and expected autonomy

Specify what to assign: instrumentation libraries, collectors, storage, data access or profiling. Distinguish delivery within a defined architecture from responsibility for choices shared by several teams.

Build an assessment grid around instrumentation, distributed systems, volumes, correlation and adoption. For each criterion, record a piece of work examined, the candidate’s explanation and the limits of their experience.

Involve a technical lead who can discuss pipelines and their trade-offs. If your company does not have this expertise, involve an expert competent in these areas in the technical assessment.

2. Examine past work

Ask the candidate to describe a telemetry pipeline they have worked on. Have them explain signal generation, processing, destinations and users.

Identify their personal contribution and the decisions made by others. Explore an SDK, integration or collector they can explain in more depth. Ask about the constraints they encountered and the trade-offs they made.

The ability to follow data throughout the pipeline and explain the choices is a positive sign. A description focused on tool brands, with no identifiable contribution, calls for further probing.

3. Discuss a case relevant to the role

Fictional example: several services emit traces and logs. Teams struggle to link these signals, and the volume stored is increasing.

Ask what information is missing before choosing a solution. Have the candidate propose an approach to examining instrumentation, context propagation and the pipeline. Ask what needs to remain accessible to users.

Then discuss cardinality, sampling and retention if these topics fit the scale of the role. Ask them to explain the expected effects on volume and uses, without requiring a single answer.

Observe whether the candidate distinguishes correlation from explaining a cause. Probe any proposal to replace a tool that comes before analysis of the signals and needs.

4. Assess support for teams

Ask how the candidate would help developers instrument an application. Have them specify what they would provide in a library or documentation and what they would ask of the application team.

Invite them to explain a collection trade-off to a non-specialist. Observe the clarity of their explanation and how they gather users’ needs.

If the role includes technical coordination, discuss how decisions are divided between teams. Do not equate technical influence with line management experience. Assess the latter separately if you intend to assign it.

5. Cross-check the responsibilities described

With the candidate’s consent, use references to clarify the contributions presented. Ask referees about component delivery, technical choices and user support.

Seek to understand the degree of autonomy actually exercised. Cross-check discrepancies between the account given and the responsibilities confirmed before reaching a conclusion.

Finally, compare the evidence gathered with the initial criteria. A gap in a specific tool should be assessed in light of the development and reasoning abilities demonstrated. A gap in a core responsibility deserves further examination.