Workbook

Make the Mission Yours

Role: Data Engineer

Use these activities to apply each principle to your current product, service, or project. These activities are a sample to get you started, not an exhaustive list. Adapt and expand them based on your team's context and needs. Capture your answers, share them with your team, and revisit them as you learn.

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Important: When Using AI Tools

When using AI-assisted activities, always double-check for accuracy and meaning each and every time. AI tools can help accelerate your work, but human judgment, validation, and critical thinking remain essential.

Review AI-generated content with your team, validate it against real user feedback and domain knowledge, and ensure it truly serves your mission and user outcomes before proceeding.

1) Shared Mission and Vision

State how pipelines support mission outcomes.

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Learn More

For more information and deeper understanding of this principle, refer to the 1) Shared Mission and Vision section in the framework.

Workbook Activities (do now)

☐Add “mission/outcome supported” to one pipeline runbook (e.g., supports activation metric).
☐Review with PM/Analyst which outcomes depend on your datasets this sprint.
☐For a new table, document the user/business question it enables.
☐Tag one in-flight task with the mission metric it supports and the consumer who needs it.
☐Explain in standup how a current pipeline change advances a specific user/business outcome.

AI Assisted Activities

☐Use AI to help draft data pipeline documentation that maps to mission outcomes, but have your team review and refine it to ensure it reflects real user needs and business goals.
☐Ask AI to generate potential data pipeline improvements based on mission outcomes, then validate each one against direct user feedback and domain knowledge before implementing.
☐Use AI to help structure your pipeline runbooks tied to mission outcomes, but ensure human team members validate that each pipeline truly serves the mission before deploying.
☐Have AI analyze past data pipeline work to identify mission alignment patterns, then use those insights in team discussions to improve how data infrastructure connects to user outcomes.

Evidence of Progress

☐Pipelines are described by the outcomes they serve.
☐Analysts/PMs acknowledge alignment to their questions.

2) Break Down Silos

Co-own contracts and data readiness.

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Learn More

For more information and deeper understanding of this principle, refer to the 2) Break Down Silos section in the framework.

Workbook Activities (do now)

☐Define data contracts with upstream/downstream owners before changes.
☐Hold a joint schema review with analysts and QA for a new dataset.
☐Add data validation steps that QA can see before launch.
☐Pair with a downstream consumer to confirm fields, semantics, and edge cases.
☐Schedule a quick “data readiness” check for the release and publish the results.

AI Assisted Activities

☐When AI generates data schemas or pipeline designs, have cross-functional team members (analysts, QA, PM) review them together to ensure they serve users and align with mission.
☐Use AI to help draft data contracts or schema documentation, but ensure all roles contribute their perspectives during the actual schema review sessions.
☐Have AI analyze data handoff patterns and schema friction, then use those insights in cross-functional discussions to improve collaboration.
☐Use AI to help structure data collaboration sessions, but ensure human team members make decisions together about what to build and how it serves users.

Evidence of Progress

☐Fewer breaking changes for downstream consumers.
☐Schema/contract is agreed before build; validation catches issues early.

3) User Engagement

See how data is consumed in real workflows.

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Learn More

For more information and deeper understanding of this principle, refer to the 3) User Engagement section in the framework.

Workbook Activities (do now)

☐Attend an analytics or ops review to watch how data is used for decisions.
☐Interview a consumer (analyst/PM/support) about pain points with current data.
☐Instrument one pipeline to surface usage/quality signals to consumers.
☐Shadow a consumer building a report; note friction and adjust schema/docs.
☐Share one consumer quote about data pain with the team and propose a fix.

AI Assisted Activities

☐Use AI to analyze consumer feedback, usage data, or quality metrics to identify patterns for data pipeline work, but always validate AI insights through direct consumer engagement or observation.
☐Have AI generate questions for consumer interviews based on your data assumptions, then use those questions in real conversations with data consumers to build genuine empathy.
☐Use AI to help summarize consumer research findings for data pipeline planning, but ensure you review the summaries and add your own observations from direct consumer interactions.
☐Have AI analyze consumer behavior patterns from data usage telemetry, then discuss those patterns with actual consumers to understand the "why" behind the behavior before making pipeline changes.

Evidence of Progress

☐You addressed a consumer pain point with a data change or documentation.
☐Consumers see quality/usage signals and respond to them.

4) Outcomes Over Outputs

Measure data work by quality and impact on decisions.

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Learn More

For more information and deeper understanding of this principle, refer to the 4) Outcomes Over Outputs section in the framework.

Workbook Activities (do now)

☐Track a quality/SLA metric for a critical dataset (freshness, completeness) and share weekly.
☐After a change, report before/after on errors or trust signals.
☐Tie one data improvement to a downstream decision it unlocked.
☐If quality missed, propose a concrete fix (contract, validation, retry) and schedule it.
☐Add a “post-release data check” to confirm the intended decision-maker can use the data.

AI Assisted Activities

☐When AI generates data pipelines or infrastructure code, define data quality outcome metrics upfront and measure whether AI-generated pipelines achieve intended user outcomes, not just technical completion.
☐Use AI to help analyze data quality outcome data and identify patterns, but have human team members interpret what those patterns mean for users and the mission.
☐Have AI help draft data quality outcome definitions and success criteria, but ensure the team validates them against real user needs and business goals before deploying.
☐Use AI to track and report on data quality outcome metrics, but schedule human team reviews to discuss what the metrics mean and how to adjust pipelines based on observed impact.

Evidence of Progress

☐Quality metrics are visible and trending in the right direction.
☐Downstream teams cite your change as enabling a decision.

5) Domain Knowledge

Map data across the service ecosystem.

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Learn More

For more information and deeper understanding of this principle, refer to the 5) Domain Knowledge section in the framework.

Workbook Activities (do now)

☐Map front/back stage data flows for a core journey; mark weakest links and owners.
☐Identify regulatory/PII constraints per table and add to docs.
☐Note which upstream systems most affect data quality and agree on monitoring with owners.
☐Review a past data incident for this domain and add one guardrail to current work.
☐Verify one critical upstream dependency this sprint and log the contact/contract in docs.

AI Assisted Activities

☐Use AI to help summarize domain documentation, data lineage, or ecosystem constraints for data pipeline work, but validate AI-generated domain knowledge through direct engagement with domain experts.
☐Have AI generate questions about domain constraints or data ecosystem relationships, then use those questions in conversations with domain experts to build deep understanding.
☐Use AI to help draft data flow maps or ecosystem diagrams, but ensure team members review them with domain experts to verify accuracy and completeness.
☐Have AI analyze past data pipeline work or domain-related issues, then discuss those insights with the team and domain experts to identify patterns and prevent similar problems.

Evidence of Progress

☐Data docs show constraints, ownership, and weak links.
☐Monitors/alerts exist for the riskiest upstream dependencies.

6) The Art of Storytelling

Explain data work as user/business value.

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Learn More

For more information and deeper understanding of this principle, refer to the 6) The Art of Storytelling section in the framework.

Workbook Activities (do now)

☐Describe a data fix as a user-impact story (e.g., “more accurate billing for customers because…”).
☐Create two summaries of a pipeline change: one for engineers (lineage, tests) and one for stakeholders (decisions it enables).
☐Show a before/after chart of data quality with the decision it unlocked.
☐Add a consumer quote to your update about how the change helps them decide faster/better.
☐Record a 60-second explainer of a pipeline change focusing on who benefits and how.

AI Assisted Activities

☐Use AI to help structure or draft data pipeline stories and documentation, but refine them with real consumer anecdotes, emotions, and personal observations from direct consumer interactions.
☐Have AI generate different versions of data pipeline updates for different audiences (engineers vs stakeholders), but ensure each version includes authentic human stories about real user impact.
☐Use AI to help summarize data pipeline work in presentations, but lead with human stories about real consumers, using AI-generated summaries as supporting material.
☐Have AI help draft data pipeline documentation or updates, but always include real consumer quotes, data points, or anecdotes that connect your data work to human impact.

Evidence of Progress

☐Stakeholders can retell why a data change mattered.
☐Consumers trust and use the improved data for decisions.