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These previews come from the published bundle files, so you can judge the method and writing before using it.
Role guide
Data Engineer Role
Use this role bundle when data engineering work needs schema discipline, source inspection, environment awareness, and review-ready implementation plans.
Read the fileOverview
Data Engineer Overview
This bundle helps teams produce source-aware data engineering plans without turning plausible schemas or platform assumptions into confident implementation guidance.
Read the fileWorkflow
Source-Aware Pipeline Triage
Use this workflow before producing a pipeline plan, schema review, quality check, or production handoff.
Read the fileTemplate
Source-Aware Data Pipeline Plan
A pipeline plan should make data assumptions and validation obligations explicit.
Read the fileIs this bundle right for your task?
Who it is for
- People performing or supporting Data Engineer work, plus teams reviewing its decisions and outputs
- Teams working in General
When to use it
- A Data Engineer task needs a structured plan, evidence checklist, or review-ready output.
- A recommendation needs its assumptions, owners, risks, dependencies, and success measures made explicit.
What you need to provide
- The task objective, intended audience, working context, constraints, source material, and decision owner.
- Relevant reports, exports, examples, policies, prior decisions, and success measures available for the task.
Tasks and expected outputs
Questions it helps answer
- Turn source-system context, schemas, transformation rules, and warehouse constraints into a reviewable data pipeline plan.
- Separate verified schema facts, user-provided assumptions, missing source evidence, and environment-specific configuration.
- Prepare data quality, lineage, orchestration, and validation guidance without inventing fields, tables, credentials, or platform behavior.
What it helps produce
- Source-aware data pipeline plan
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Provide the task objective, intended audience, working context, constraints, source material, and decision owner. Ask the agent to approach Data Engineer work by producing Source-aware data pipeline plan with a prioritized plan, evidence checks, owners, risks, and unresolved questions. Begin with O*NET Database Architects profile, including Data Engineer reported job title, then confirm that the reference is current and applicable. Inspect Data Engineer Role before drafting.
Context path: bundles/roles/data-engineer
What the bundle includes
Frameworks
- source-evidence matrix
- schema-grounding protocol
- data-quality validation gate
Evaluations
- Data engineering source-awareness check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Treating the bundle as a substitute for organization-specific authority, firsthand evidence, or accountable review.
Known limitations
- This bundle supports data engineering planning and review; it is not security, privacy, legal, audit, or production-change approval.
- Environment-specific recommendations require current schemas, data samples, access controls, contracts, platform configuration, and owner review.
- Do not invent table names, fields, metric definitions, pipeline behavior, credentials, costs, SLAs, lineage, or data quality results.
Safety notes
- Minimize personal, customer, patient, employee, financial, credential, and regulated data in prompts and examples.
- Require explicit confirmation before modifying production pipelines, schemas, permissions, jobs, exports, retention settings, or infrastructure.
- Route privacy, security, compliance, and production-impacting changes to accountable reviewers.