Explore data engineering OKR examples with objectives and measurable key results. Choose an example that fits your situation, then adapt its targets to your baseline.
Clear objectivesMeasurable key resultsBuilt for a weekly check-in
What is a good data engineering OKR?
A good data engineering OKR pairs one clear objective with two to four measurable key results the team can score every week. It describes an outcome rather than a list of activities, sets targets that are ambitious but scoreable, and is reviewed weekly so blockers surface before the quarter gets away from the team.
START WITH THE OUTCOME
What does your team want to improve?
Pick a starting point. Adjust the targets to your baseline.
Choose the data engineering outcome you need to improve. Record the current value, target, owner, and measurement source for each key result before starting your planning cycle.
01
Set your baseline. Check where you are today, then choose targets that stretch your team.
02
Agree on ownership. Give each key result a clear owner and a source of data.
03
Check in every week. Review the numbers, share confidence, and decide what needs attention.
KNOW WHAT TO MEASURE
The numbers behind your goals.
Choose a small set of KPIs to watch alongside your OKRs.
Clear objectives