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analytics-data-engineering-manager-product

Skill8 starsChanged 6 months agoMITView on GitHub

Guides managers of product-embedded analytics engineering teams—org design, hiring and levels, roadmap and prioritization with product managers, analytics data product delivery, squad alignment, stakeholder forums, team KPIs, and escalation paths for metric and mart quality. Use when leading analytics engineers on product domains, planning analytics roadmaps per product area, resolving prioritization across squads, or defining how marts and metrics ship with features—not for hands-on dbt/SQL (analytics-data-engineer), enterprise data platform ADRs (data-architect), company-wide data governance ops (data-manager), or dashboard UX (bi-analyst). For vertical copilot/RAG product engineering management, use engineering-manager-vertical-ai-products—not analytics-data-engineering-manager-product.

1 other project uses a version of this.

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We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

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    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run delivery cadence: intake, estimation, dependencies with app/data platform teams

    line 22
  2. Very specific:

    No redefining enterprise architecture without data-architect ADR

    line 90

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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SKILL.md

Analytics Data Engineering Manager, Product

When to Use

  • Design or scale a product-embedded analytics engineering org
  • Prioritize analytics backlog with product managers and domain leads
  • Define analytics data products (marts, metrics packs) tied to product launches
  • Run delivery cadence: intake, estimation, dependencies with app/data platform teams
  • Set team KPIs (freshness, test pass rate, time-to-metric for launches)
  • Hire, level, and develop analytics engineers and leads
  • Escalate cross-squad conflicts (metric definitions, shared dimensions, capacity)

When NOT to Use

  • Writing or refactoring dbt models → analytics-data-engineer
  • Warehouse partition tuning or ETL platform design → data-warehouse-engineer
  • Enterprise mesh, governance program, catalog policy → data-architect or data-manager
  • Dashboard design and executive storytelling → bi-analyst
  • Multi-portfolio technical programs (non-analytics) → technical-program-manager
  • BRD/process mapping without analytics delivery → business-analyst

Related skills

NeedSkill
dbt implementation detailanalytics-data-engineer
Org-wide data ops and governance cadencedata-manager
BI and KPI storytellingbi-analyst
Business rules sign-offbusiness-analyst
Platform architecturedata-architect
Large cross-functional programtechnical-program-manager

Core Workflows

1. Org design and squad model

Embedded vs centralized analytics engineering; ratios; interfaces to DE and BI.

See references/team_org_design.md.

2. Roadmap and prioritization

Product-domain backlog, launch-aligned milestones, capacity trade-offs.

See references/roadmap_prioritization.md.

3. Delivery and launch alignment

Definition of done for analytics with feature releases; dependency management.

See references/delivery_launch_alignment.md.

4. Stakeholder partnerships

PM, product analytics, finance, legal/privacy, platform engineering forums.

See references/stakeholder_partnerships.md.

5. Hiring and career development

Levels, interview loops, growth plans, performance calibration inputs.

See references/hiring_development.md.

6. Metrics and accountability

Team scorecard, product analytics SLAs, incident and quality escalation.

See references/team_metrics_accountability.md.

Output standards

  • Roadmap items link business outcome, metric/mart deliverable, and owner
  • Launch checklist signed by PM + analytics eng before GA
  • Escalations documented with options and recommendation
  • No redefining enterprise architecture without data-architect ADR

When to load references

  • Org → references/team_org_design.md
  • Roadmap → references/roadmap_prioritization.md
  • Delivery → references/delivery_launch_alignment.md
  • Stakeholders → references/stakeholder_partnerships.md
  • People → references/hiring_development.md
  • KPIs → references/team_metrics_accountability.md

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