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Data Engineering RFP Checklist

Complete template for evaluating data platform vendors and consultants

How to Use This Checklist

Purpose: Ensure you ask the right questions and evaluate vendors consistently across technical, commercial, and operational dimensions.

Scoring: Rate each vendor 1-5 on each criterion. Weight categories based on your priorities.

Timeline: Allow 3-4 weeks for vendor responses, plus 2 weeks for evaluation and interviews.

1. Project Scope & Requirements

Business Objectives

Technical Requirements

2. Vendor Capability Assessment

Criterion Must Ask Red Flags Score (1-5)
Relevant Experience 3+ case studies in your industry, scale, tech stack Generic case studies, no verifiable references
Team Composition Named engineers with resumes, seniority mix "TBD" team members, all juniors or all seniors
Technical Approach Architecture proposal, trade-off discussions One-size-fits-all architecture, no customization
Knowledge Transfer Training plan, documentation standards, handoff process Vague "we'll document as we go"
Cost Transparency Itemized breakdown, what's included/excluded Ballpark estimates, hidden fees in fine print

3. Commercial Terms Checklist

✅ Must Have

❌ Avoid

4. Critical Questions for Vendors

On Team Staffing:

  • "Can I interview the proposed team members before signing?"
  • "What's your policy on team changes mid-project?"
  • "What's the guaranteed time commitment per week for the tech lead?"

On Methodology:

  • "Walk me through a typical sprint/iteration. What's the cadence?"
  • "How do you handle scope creep and changing requirements?"
  • "What's your testing strategy for data pipelines?"

On Risk & Contingency:

  • "What are the top 3 risks to this project's timeline?"
  • "Show me a project that went poorly. What happened?"
  • "What's your escalation process when things go off-track?"

On Post-Launch:

  • "What does 'day 2 operations' support look like?"
  • "What documentation/runbooks will you provide?"
  • "Is there a warranty period? How long?"

5. Vendor Scoring Template

Score each vendor 1-5 on these weighted criteria:

Technical Expertise (30%) Architecture, team skills, platform knowledge
Relevant Experience (25%) Case studies, references, industry fit
Cost & Value (20%) Total cost, payment terms, ROI potential
Communication & Fit (15%) Responsiveness, cultural fit, transparency
Risk Factors (10%) Contract terms, team stability, methodology

✅ Final Decision Criteria:

Shortlist vendors scoring 4.0+ overall. Conduct technical deep-dives with top 2-3. Make decision within 1 week of final interviews.

Ready to Evaluate Vendors?

Compare 20 pre-vetted data engineering companies based on expertise, pricing, and fit.