Find your next good goal.

Objective

Enhance accuracy in story estimation by software engineering team

1. Achieve 90% team satisfaction rate with the improved estimation process
2. Reduce average estimation error to less than 10% in next 100 tasks
3. Train 100% of the team on advanced estimation techniques and methods

Objective

Enhance engineering team's productivity

1. Increase project completion rate by 20%
2. Implement a new efficiency-enhancing tool with full adoption by team
3. Reduce engineering errors by 15%

Objective

Improve interoperability between data engineering teams

1. Offer biweekly data interoperability training to 90% of data engineering teams
2. Reduce cross-team data discrepancies by 50%, ensuring increased data consistency
3. Implement standardized data protocols across all teams increasing cross-collaboration by 30%

Objective

Foster continuous improvement on Engineering metrics

1. Achieve a 10% increase in team's average productivity metrics
2. Implement weekly trend analysis reports for every team member
3. Reduce error rates in engineering processes by 15%

Objective

Improve satisfaction and find improvements for engineering stakeholders

1. Identify and initiate 3 actionable improvement opportunities from stakeholder feedback
2. Increase stakeholder satisfaction rate by 25% as measured by quarterly surveys
3. Reduce issue resolution time by 20% to boost stakeholder content

Objective

Achieve optimal efficiency in engineering operations

1. Lower engineering error rates by 10% through enhanced quality control measures
2. Reduce operation downtime by 20% through improved preventative maintenance procedures
3. Increase operational efficiency by 15% via process improvement initiatives

Objective

Enhance data engineering capabilities to drive software innovation

1. Improve data quality by implementing automated data validation and monitoring processes
2. Enhance software scalability by optimizing data storage and retrieval mechanisms for large datasets
3. Increase data processing efficiency by optimizing data ingestion pipelines and reducing processing time

Objective

Improve software engineering skills through effective time management

1. Increase the number of completed coding tasks by 20% each week
2. Improve adherence to project deadlines by delivering work on time in 90% of cases
3. Reduce time spent on non-essential activities by 15% during work hours

Objective

Significantly increase the speed of our development cycle

1. Improve developer NPS from 20 to 60
2. Reduce build times from 25 to under 5 mins
3. Reduce cycle time from 28 days to 6 days

Objective

Build a world-class infrastructure

1. Increase Apdex above 0.95
2. Reduce build time to be under 5 minutes
3. Our stress tests show that we can support 10,000 concurrent users

Objective

Cut rework between design and engineering

1. Reduce design-related rework from 25% to 8% of tickets
2. Hold design reviews with engineering on 100% of features before build, up from 30%
3. Raise engineering satisfaction with design specs from 3.2 to 4.3 out of 5

Objective

Free up engineering time lost to legacy code

1. Reduce build time from 25 minutes to 8 minutes
2. Cut time spent on unplanned maintenance from 35% to 20% of sprint capacity
3. Remove 3 deprecated services and their dependencies

Objective

Increase delivery speed while keeping change failure low

1. Increase deploys to production from 2 per week to daily
2. Reduce lead time for changes from 8 days to 2 days
3. Keep change failure rate under 10%, down from 18%

Objective

Make our product something customers can rely on

1. Reduce customer-facing incidents from 12 to 5 per quarter
2. Cut mean time to recovery from 3 hours to 45 minutes
3. Complete post-incident actions for 100% of major incidents within 2 weeks, up from 50%

Objective

Hit agreed SLOs on our most critical services

1. Raise API availability from 99.5% to 99.9%
2. Bring p95 response time on core endpoints from 900ms to 400ms
3. Define and publish SLOs for all 8 tier-1 services, up from 2

Objective

Grow usage without cloud spend growing at the same rate

1. Reduce infrastructure cost per active customer from $4.20 to $3.00
2. Tag 95% of cloud resources to an owning team, up from 60%
3. Cut idle and unused resources from 18% to 5% of spend

Objective

Shorten time to first meaningful contribution for new engineers

1. Reduce time to first merged pull request from 10 days to 3 days
2. Get local environment setup under 1 hour, down from 1 day
3. Raise new-hire onboarding satisfaction from 6.5 to 8.5 out of 10

Objective

Catch the bugs customers would see before release

1. Reduce bugs found in production from 40 to 15 per month
2. Raise automated test coverage on critical user flows from 45% to 85%
3. Cut flaky test rate from 8% to 1%

Objective

Close the highest-risk security gaps before they're found

1. Patch 100% of critical vulnerabilities within 7 days, up from 60%
2. Enforce SSO and MFA on 100% of internal tools, up from 70%
3. Reduce open high-severity findings from 45 to 10

Objective

Move a critical workload to the new platform with no customer impact

1. Migrate 100% of traffic from the legacy system, up from 0%
2. Keep customer-facing errors during migration under 0.1%
3. Decommission the legacy system and cut its $15k monthly cost to $0

Objective

Reduce after-hours pages without missing real issues

1. Cut after-hours pages from 30 to 8 per week
2. Reduce non-actionable alerts from 60% to 15% of all alerts
3. Raise on-call satisfaction from 4 to 7 out of 10

Objective

Drive data culture and adoption throughout the NASDAQ company

1. Achieve a 15% decrease in data-related issues reported
2. Implement 2 company-wide data literacy workshops
3. Increase department use of data tools by 30%

Objective

Ensure adoption and compliance with circuit breaker standards

1. Implement two new practices or technologies that promote circuit breaker standards compliance
2. Conduct five workshops on circuit breaker standards for relevant employees
3. Achieve 100% completion of compliance checklists by all relevant departments

Objective

Enhance the performance of Databricks pipelines

1. Implement pipeline optimization changes in at least 10 projects
2. Reduce the processing time of pipeline workflows by 30%
3. Increase pipeline data load speed by 25%