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Data Engineering RFP Checklist (2026)

Forty weighted criteria across scope, team, commercials, and EU AI Act readiness. This is the 2026 version, for buyers evaluating Snowflake, Databricks, BigQuery, or Microsoft Fabric vendors.

Last reviewed 8 May 2026 · By Peter Korpak, Chief Analyst

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How to Use This Checklist

Purpose: Ask every vendor the same questions and score them on the same technical, commercial, and operational criteria.

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:

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.

6. What Are the Most Common Data Engineering RFP Mistakes?

The single most common data engineering RFP mistake is pre-specifying the stack - writing the RFP around whatever platform your team already knows. It appears in nearly every flawed RFP alongside vague success criteria; together they make rigorous vendor evaluation nearly impossible from day one.

# Mistake Why It Hurts
1 Pre-specifying the stack Locks vendors into pitching your platform bias instead of the right architecture; $200k-$420k rework when the mismatch surfaces.
2 Vague success criteria Unenforceable goals let any proposal appear to meet the RFP; drives $120k-$280k in scope disputes.
3 No pre-RFP market scan The best-fit vendor is never invited; the miss shows up as 4-8 weeks lost to the wrong partner.
4 Sending to 10+ vendors Response volume drowns the evaluation team; selection defaults to the best presenter, not the best fit.
5 No paid pilot Free POCs get staffed by pre-sales architects who won't be on the project; $180k-$380k rework follows.
6 Vendor-led requirements gathering Vendors scope discovery toward their own strengths, so the requirements document is really a proposal in disguise.
7 Fixed-price for unscoped work Vendors price in a 35-60% risk premium you pay regardless of whether the risk ever materializes.
8 No EU AI Act / data-residency probe Fine exposure up to €35M under Regulation EU 2024/1689; remediation alone can run $350k+.
9 Pre-sales staffing bait The architects who wrote the winning proposal aren't the engineers who show up to deliver it.
10 Cost weighted above 25% Rewards the most aggressive bidder over the best delivery team; cost correlates poorly with quality.
11 No change-order rate cap Lets vendors bill out-of-scope work at any rate; can double contract value on complex migrations.
12 Case studies without scale match A vendor's 100GB success story says nothing about their ability to deliver your 10TB project.
13 Loudest stakeholder dominates scoring Group discussion amplifies confidence, not capability, and skews the pick before evidence is compared.
14 No architecture working session Pitch decks reward presentation skill, not the engineering judgment a real backlog problem would reveal.
15 No 90-day off-ramp clause Without a contractual exit, you're negotiating a failing engagement from zero leverage.

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