Engineering OKRs: A Practical Guide with 20 Examples for Engineering Teams

A sales team of engineers may deliver every sprint and fail to make improvements to the product.

I have seen engineers ship features on schedule with the number of incidents rising, technical debt making development harder, and customers struggling with the same issues. It’s not the problem of effort here but rather the problem of understanding what outcomes are important.

According to the Stack Overflow Developer Survey 2024, technical debt became the top frustration at work for professional developers (63%).

These are the reasons why engineering teams should pursue not just the outcomes related to tickets completed or features shipped but other types of goals.

OKRs for engineering teams transform high-level priorities such as improved performance, reduced technical debt, and faster releases into tangible outcomes.

In this article, you will learn about the differences between engineering and product OKRs, how to create effective engineering OKRs, and explore 25 practical engineering OKR examples.

Why Use OKRs for Engineering Teams?

Engineering teams seldom lack tasks to perform. Their real challenge is selecting the work that will create the most meaningful improvement.

OKRs for engineering teams go beyond backlogs and roadmaps by defining the desired outcome and explaining how success will be measured.

Examples include:

Objective: Ensure more reliable payments.

Key results:

• Increase the successful rate of payments from 94% to 98%.
• Decrease the number of incidents involving payments from 12 to less than four per quarter.
• Reduce the average resolution time from 90 minutes to 30 minutes.

This way, an objective provides the team with a sense of direction and defines what terms like “faster” and “reliable” mean.

Properly designed OKRs also help teams establish priorities and balance competing objectives. In other words, the team may strive to release software more frequently, but it should not compromise its stability. Thus, it could measure the deployment frequency along with the failure rates and time to recovery.

Engineering outcomes are directly related to product outcomes; hence, the need to understand how they differ from each other

Difference between Product OKRs and Engineering OKRs

While the engineering and product teams may be working on the same project, there is a difference in what they each have to do for its success.

The product OKRs are more centered around the customer or business impact that the product must deliver. Engineering OKRs are concerned with capabilities and system enhancements.

Now, for instance, let’s consider an organization that wishes to improve its customer onboarding process.

The product team can concentrate on enabling greater adoption of customers through onboarding, activation, and value realization of the product during the first week.

The engineering team can enable the same thing through improved page load times, integration success rate, workflow reliability, and issue resolution time.

While both teams work towards the same thing, they own different metrics. The product owns customer adoption, and engineering owns the technical experience behind it.

This difference enables engineering teams to leverage OKRs for tackling technical problems that typically lie in the backlog.

Using OKRs To Overcome Technical Challenges

Technical challenges seldom happen in isolation.

Delay in releases can stem from unstable automated tests, long code reviews, complex dependencies, ambiguous ownership of the services, or an unstable development environment.

Low reliability can stem from ineffective monitoring, defects, insufficient capacity, absence of a recovery plan, and delayed response to incidents.

The complexity of these issues means that engineering teams react by setting up a large number of initiatives. They enhance their tools, rewrite services, automate testing, change architectures, or analyze incidents.

Such initiatives can be necessary, but the completion of these initiatives does not mean that the initial challenge is addressed.

An OKR keeps the engineering team focused on the effect these initiatives will bring about.

How to Write Good OKRs for Engineers

1. Know How OKRs Work

In OKRs, the objective is how the team aims to improve, whereas the key results measure success.

Objective: Create a more reliable production environment.

Key Results:

• Decrease critical incidents from eight to fewer than three quarterly.
• Decrease detection time from 15 minutes to five minutes.
• Decrease recovery time from 90 minutes to 30 minutes.

The task of introducing monitoring tools and reviewing incidents would be considered initiatives but, not a key result. A good KR makes a noticeable difference to engineers or customers.

2. Collect Input

OKRs for engineering teams should combine leadership priorities with input from engineering, product, quality, security, infrastructure, and customer support teams. 

Data can help us discover:

• Recurring customer problems
• Systems that require too much engineering effort
• Recurring delays in releases
• Incidents
• Technical debt

This allows teams to pick issues that are meaningful, measurable, and controllable.

3. Choose Focus Areas

An engineering team cannot enhance everything in one cycle. Select one or two most important areas:

Functionality: Completeness of workflow, integration strategy, and defects

Performance: Response time, processing speed, and infrastructure efficiency

Usability: Task completion, user errors, and support calls

Reliability: Availability, incidents, detection, and recovery time

Data Security: Vulnerabilities, access control, and remediation time

Development Speed: Lead time, deployments, reviews, builds, and tests

Focus needs to align with organizational priorities and the largest technical limitation for the engineering team.

4. Define the OKRs

Begin by identifying the problem, specifying the required improvement, and determining the starting point. Pick two to four key result indicators.

Make sure the outcomes balance each other, for example speed and quality or availability and cost. Set one responsible person for each OKR, and list projects in a separate section.

The target numbers of the following engineering OKR examples are indicative and should be tailored to the team’s baseline and capacity.

20 Engineering OKR Examples

Functionality

1. Enhance a Key Customer Process

Objective: Make account setup process consistently successful for new customers.

Key Results:

• Boost account setup success rate from 82% to 95%.
• Lower functional errors in account setup from 7% to under 2%.
• Boost customers setting up their accounts without any help from 68% to 85%.

2. Enhance Third-Party Integration

Objective: Make external integration reliable for customers and our team.

Key results:

• Boost successful integration requests from 96% to 99.5%.
• Lower number of support tickets due to integration issues from 120 to fewer than 50 per month.
• Decrease failed data synchronization requiring manual intervention from 8% to under 2%.

3. Enhance the Quality of New Feature Releases

Objective: Deliver features as designed in their initial production release.

Key results:

• Decrease the number of high-priority functional defects identified in the first 30 days of release from 18 to less than six.
• Increase acceptance test pass rate of key workflows from 87% to 97%.
• Decrease the number of new features that need significant post-release fixes from 20% to less than 8%.

Performance

4. Speed Up Application Response Times

Objective: Make sure that core application response times are always fast.

Key results:

• Bring down the 95th percentile response time of core APIs from 1.8 seconds to less than 800 milliseconds.
• Bring down the 95th percentile load time of three most popular screens from 4.2 seconds to less than 2.5 seconds.
• Make sure that target response times are achieved even at peak loads of 150% of current average load.

5. Increase Data Processing Efficiency

Objective: Serve the data to the users as fast as possible.

Key results:

• Decrease average data processing time from 45 minutes to less than 15 minutes.
• Increase percentage of scheduled data jobs served in the expected window from 86% to 98%.
• Decrease processing failures due to capacity limits from 14 to less than three per month.

6. Increase Infrastructure Cost Efficiency

Objective: To support growth of the product without growth in infrastructure costs.

Key results:

• Decrease infrastructure cost per active user by 20%.
• Increase the average utilization of selected computing resources from 38% to 60%.
• Decrease or resize 90% of underutilized computing resources without impacting availability.

Usability

7. Simplify Onboarding Experience for New Users

Objective: Help users accomplish something of value easily in the beginning.

Key results:

• Experience an improvement of 64% to 82% in the onboarding success rate.
• Onboarding process takes less than 10 minutes (currently 18 minutes).
• Lessen the onboarding assistance calls by 40%.

8. Improve Availability of Crucial User Flows

Objective: Maximize availability of critical user flows to the largest possible customer base.

Key results:

• Fix all major accessibility problems for the first five most crucial workflows.
• Get task completion with just a keyboard for the first five workflows to 95% (currently 70%).
• Reduce complaints about accessibility by 60%.

9. Enhance Product Search Functionality

Objective: Assist users in finding what they are looking for.

Key results:

• Raise the number of searches ending in finding an appropriate selection from 58% to 75%.
• Reduce percentage of searches that yield no useful results from 16% to below 7%.
• Decrease search-to-selection time to below 45 seconds (from 90 seconds).

Reliability

10. Increase the Availability of Critical Services

Objective : Ensure availability of critical services to customers.

Key results :

• Availability of critical services – increase from 99.7% to 99.95%
• Number of critical incidents that have customer impact – reduce from eight to fewer than three per quarter
• Downtime due to critical incidents affecting customers – reduce from 130 minutes to fewer than 30 minutes per quarter

11. Improve Early Detection and Resolution of Incidents

Objectives: Detect and remediate any production incidents to prevent customer impact from prolonged downtime.

Key results:

• Mean time to detect critical incidents – reduce from 15 minutes to below five minutes
• Mean time to restore service – reduce from 90 minutes to below 30 minutes
• Critical incidents detected through monitoring and not customer reported – increase from 55% to 90%

12. Ensure Disaster-Recovery Capability

Objective: Restore critical services/data in the event of a major incident with confidence.

Key results:

• Recovery time of critical services – reduced from six hours to below two hours
• Recovery-point target met in 100% of disaster recovery simulations.
• Successful recovery simulation completed for all critical services without undocumented manual dependencies.

Data Security

13. Minimise Critical Security Risks

Objective: Mitigate severe security threats before they impact customers or operations.

Key results:

• Lower critical and high exposure vulnerabilities from 42 to less than eight.
• Decrease average remediation time for critical vulnerabilities from 18 days to less than five days.
• Automate security scanning of 100 percent of production repositories and build pipelines.

14. Improve Access Control

Objective: To make sure that sensitive systems and data are accessible by authorized users only.

Key outcomes:

• Increased number of privileged account multi-factor authentication from 72% to 100%
• Reduction of the number of idle privileged accounts from 35 to zero.
• Completing access reviews for all systems that have sensitive information.

15. Increase the Security of Sensitive Data

Objective: Protect sensitive data consistently across its entire lifecycle.

Key results:

• Raise encryption of identified sensitive data stores from 80 percent to 100 percent.
• Decrease unclassified sensitive data fields from 240 to less than 20.
• Eliminate production data access with no business need from 25 events per month to zero.

Development Speed

16. Shorten Lead Time From Code To Production

Objective: Release reliable changes to customers on a more frequent basis.

Key Results:

• Decrease median lead time from eight days to three days.
• Increase number of successful production deployments from two to five per week.
• Keep deployments that result in production errors below 5%.

17. Improve the Code Review Process

Objective: Help engineers get their code reviewed and make deployments without any unnecessary delays.

Key Results:

• Decrease median time to the first code review response from 18 hours to less than six hours.
• Decrease the amount of pull requests being open for more than three working days from 30% to less than 10%.
• Decrease the percentage of changes needing extensive rework after the first review from 22% to 12%.

18. Test Quickly While Maintaining Quality

Objective: Allow engineers to get quick feedback on the safety of deploying new code.

Key Achievements:

• Reduce average CI test duration from 35 minutes to less than 15 minutes.
• Boost automated coverage of critical regression tests from 65% to 90%.
• Decrease flaky test errors from 12% to less than 2% of test run attempts.

19. Increase System Scalability

Objective: Guarantee that critical systems have the capacity to cope with increased customer demand without any decrease in performance.

Key Results:

• Scale up the number of users that can be served simultaneously from 10,000 to 25,000 without decreasing performance.
• Decrease the average response time during peak traffic from 2.5 seconds to less than one second.
• Conduct load tests successfully for all critical customer-facing services.
• Have no infrastructure failures during peak traffic.

20. Reduce Technical Debt

Objective: Increase the maintainability and stability of critical systems by eliminating high-priority technical debts.

Key Results:

• Decrease high-priority technical debt items from 75 to less than 25.
• Refactor the five most problematic modules that generate the most production incidents.
• Increase the number of critical services that meet code quality standards from 68% to 90%.
• Decrease production incidents caused by legacy code from 12 to less than four per quarter.

Improve Engineering Work With OKRs

The best OKRs can be used by engineers for prioritisation, assessing progress, and making necessary adjustments.

Link OKRs With Engineering Projects

Each key result needs to be linked to the specific projects that will affect it. Refactoring can be linked to the technical-debt KR, while monitoring can lead to improvements in recovery time.

In case the project has been done yet the KR has not improved, then it is necessary to check the initial assumptions and the reasons behind it.

Monitor Progress Frequently

Engineering OKRs need to be reviewed on a weekly or bi-weekly basis instead of monthly or quarterly ones. Pay attention to changes, their causes, blockers, and the performance of the current approach.

Balance Speed and Quality

It is not good to have the success of engineering based only on one metric. Deployment frequency needs to be balanced with failure rates, lead times with defects, and availability with recovery times.

Focus on What Matters

All operational metrics do not have to be turned into key results. Dashboards are enough for standard tracking, while OKRs should be used when the improvement really matters, is something that could be impacted by the team, and needs focus.

Learn from OKRs

Engineering is a process of trial and error. If an engineering initiative fails to deliver the desired result, the team should analyze what went wrong and adjust their strategy instead of keeping quiet about it.

Do Not Rate Individuals Based on Team OKRs

Engineering outcomes are often determined by common systems and decisions. OKRs may help discuss performance, but it should not be based solely on them.

Here’s How JOP Could Help

Engineer OKR management could get complicated if goals, projects, owners, and update status are all kept separately in different systems.

JOP makes everything simpler by linking engineer OKRs to those of the product and the whole organization. People will be able to establish clear ownership, connect projects and action points to key results, and follow progress updates in regular check-ins.

It will also help engineering managers to uncover any dependencies, obstacles, and weak spots. The idea is not to add more bureaucracy but rather to make priorities and progress visible.

Ultimately, the best engineering OKR examples measure more than completed work. They show how the product and engineering processes have meaningfully improved.