Data Engineering RFP Checklist (2026)
Forty weighted criteria across scope, team, commercials, and EU AI Act readiness — the 2026 version of the playbook used by Snowflake, Databricks, BigQuery, and Microsoft Fabric buyers.
Last reviewed 8 May 2026 · By Peter Korpak, Chief Analyst
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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 |
2.5 New for 2026: AI Act & Native-AI Readiness
Three criteria that did not appear on a 2024 RFP and now belong in the must-have column. Score each vendor 1–5 and weight at 10–15% combined if your roadmap touches AI workloads or EU data.
| Criterion | What to Ask For | Disqualifying Answer | Score (1-5) |
|---|---|---|---|
| EU AI Act Readiness | Written statement on how delivery maps to GPAI obligations (Aug 2026), Article 50 transparency duties, and high-risk system controls. Named compliance lead. | "That's the customer's responsibility." Or no awareness of the August 2026 enforcement date. | |
| Native-AI Platform Fluency | Production case study using Snowflake Cortex AI Functions, Databricks Mosaic AI Agent Framework, or Microsoft Fabric Data Agents. Cost guardrails and evaluation harness must be shown. | Slide-deck familiarity only. Or "we use the OpenAI API" without platform-native context. | |
| AI-Assisted Delivery Discipline | How AI-augmented coding (Copilot, Cursor, Claude Code) is governed: human review loop, eval harness, token-cost reporting, and how productivity gains flow into pricing. | "Our engineers use AI tools" with no governance, no evals, and full senior rates regardless of AI lift. |
Pair this section with the deeper write-up in our 35-criterion 2026 evaluation rubric and the five-stage partner selection framework.
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:
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.
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