What are Data Engineers OKRs?
The Objective and Key Results (OKR) framework is a simple goal-setting methodology that was introduced at Intel by Andy Grove in the 70s. It became popular after John Doerr introduced it to Google in the 90s, and it's now used by teams of all sizes to set and track ambitious goals at scale.
Formulating strong OKRs can be a complex endeavor, particularly for first-timers. Prioritizing outcomes over projects is crucial when developing your plans.
To aid you in setting your goals, we have compiled a collection of OKR examples customized for Data Engineers. Take a look at the templates below for inspiration and guidance.
If you want to learn more about the framework, you can read our OKR guide online.
How to write your own Data Engineers OKRs
1. Get tailored OKRs with an AI
You'll find some examples below, but it's likely that you have very specific needs that won't be covered.
You can use Tability's AI generator to create tailored OKRs based on your specific context. Tability can turn your objective description into a fully editable OKR template -- including tips to help you refine your goals.
- 1. Go to Tability's plan editor
- 2. Click on the "Generate goals using AI" button
- 3. Use natural language to describe your goals
Tability will then use your prompt to generate a fully editable OKR template.
Watch the video below to see it in action 👇
Option 2. Optimise existing OKRs with Tability Feedback tool
If you already have existing goals, and you want to improve them. You can use Tability's AI feedback to help you.
- 1. Go to Tability's plan editor
- 2. Add your existing OKRs (you can import them from a spreadsheet)
- 3. Click on "Generate analysis"
Tability will scan your OKRs and offer different suggestions to improve them. This can range from a small rewrite of a statement to make it clearer to a complete rewrite of the entire OKR.
You can then decide to accept the suggestions or dismiss them if you don't agree.
Option 3. Use the free OKR generator
If you're just looking for some quick inspiration, you can also use our free OKR generator to get a template.
Unlike with Tability, you won't be able to iterate on the templates, but this is still a great way to get started.
Data Engineers OKRs examples
You will find in the next section many different Data Engineers Objectives and Key Results. We've included strategic initiatives in our templates to give you a better idea of the different between the key results (how we measure progress), and the initiatives (what we do to achieve the results).
Hope you'll find this helpful!
OKRs to improve interoperability between data engineering teams
- ObjectiveImprove interoperability between data engineering teams
- KROffer biweekly data interoperability training to 90% of data engineering teams
- Identify 90% of data engineering teams for training
- Develop a biweekly interoperability training schedule
- Implement and monitor the data interoperability training
- KRReduce cross-team data discrepancies by 50%, ensuring increased data consistency
- Regularly audit and correct data discrepancies across all teams
- Implement a standardized data entry and management process for all teams
- Utilize data synchronization tools for seamless data integration
- KRImplement standardized data protocols across all teams increasing cross-collaboration by 30%
- Train teams on new standardized protocols
- Identify current data protocols in each team
- Draft and propose unified data protocols
OKRs to improve the quality of the data
- ObjectiveSignificantly improve the quality of the data
- KRReduce the number of data capture errors by 30%
- KRReduce delay for data availability from 24h to 4h
- KRClose top 10 issues relating to data accuracy
OKRs to reduce the cost of integrating data sources
- ObjectiveReduce the cost of data integration
- KRDecrease the time to integrate new data sources from 2 days to 4h
- Migrate data sources to Segment
- Create a shared library to streamline integrations
- KRReduce the time to create new dashboards from 4 days to <1h
- Adopt BI tool to allow users to create their own dashboards
- KR10 teams have used successfully a self-serve dashboard creation system
Data Engineers OKR best practices
Generally speaking, your objectives should be ambitious yet achievable, and your key results should be measurable and time-bound (using the SMART framework can be helpful). It is also recommended to list strategic initiatives under your key results, as it'll help you avoid the common mistake of listing projects in your KRs.
Here are a couple of best practices extracted from our OKR implementation guide 👇
Tip #1: Limit the number of key results
Having too many OKRs is the #1 mistake that teams make when adopting the framework. The problem with tracking too many competing goals is that it will be hard for your team to know what really matters.
We recommend having 3-4 objectives, and 3-4 key results per objective. A platform like Tability can run audits on your data to help you identify the plans that have too many goals.
Tip #2: Commit to weekly OKR check-ins
Setting good goals can be challenging, but without regular check-ins, your team will struggle to make progress. We recommend that you track your OKRs weekly to get the full benefits from the framework.
Being able to see trends for your key results will also keep yourself honest.
Tip #3: No more than 2 yellow statuses in a row
Yes, this is another tip for goal-tracking instead of goal-setting (but you'll get plenty of OKR examples above). But, once you have your goals defined, it will be your ability to keep the right sense of urgency that will make the difference.
As a rule of thumb, it's best to avoid having more than 2 yellow/at risk statuses in a row.
Make a call on the 3rd update. You should be either back on track, or off track. This sounds harsh but it's the best way to signal risks early enough to fix things.
How to track your Data Engineers OKRs
Quarterly OKRs should have weekly updates to get all the benefits from the framework. Reviewing progress periodically has several advantages:
- It brings the goals back to the top of the mind
- It will highlight poorly set OKRs
- It will surface execution risks
- It improves transparency and accountability
We recommend using a spreadsheet for your first OKRs cycle. You'll need to get familiar with the scoring and tracking first. Then, you can scale your OKRs process by using a proper OKR-tracking tool for it.
If you're not yet set on a tool, you can check out the 5 best OKR tracking templates guide to find the best way to monitor progress during the quarter.
More Data Engineers OKR templates
We have more templates to help you draft your team goals and OKRs.
OKRs to establish a substantial presence on YouTube OKRs to minimize escalation cases in customer service OKRs to improve efficiency in team support OKRs to successfully implement the new bank branch opening infrastructure OKRs to substantially reduce technical debt across all projects OKRs to enhance talent retention strategies