Data Consulting RFP Template

Required Information

1. A.1

   Company Overview

   Provide company history, founding year, headquarters location, and ownership structure (private, public, PE-backed).

2. A.2

   Data & AI Practice Size

   How many employees are dedicated to data engineering, analytics, and AI/ML specifically? Provide breakdown by role type (architects, engineers, data scientists).

3. A.3

   Relevant Industry Experience

   List 3-5 projects completed in our industry [INSERT INDUSTRY] within the past 24 months. Include project scope, duration, and business outcomes achieved.

4. A.4

   Client References

   Provide 3 client references we can contact, preferably in similar industries or with similar project scopes. Include project type, duration, and contact information.

5. A.5

   Partnership Tier Status

   List all cloud and data platform partnerships (AWS, Azure, GCP, Snowflake, Databricks) with tier levels (Premier, Elite, Select, Standard). Provide verification links.

6. A.6

   Financial Stability

   Provide evidence of financial stability: annual revenue (if public), years in operation, largest contract value handled, and professional liability insurance coverage.

7. A.7

   Geographic Coverage

   Where are your data professionals located? Can you support our time zones? Do you have on-site capability in [INSERT LOCATIONS]?

8. A.8

   Recent Growth & Trajectory

   Describe your data practice growth over the past 2-3 years. Any recent acquisitions, mergers, or significant capability additions?

Platform & Technology Expertise

9. B.1

   Cloud Platform Expertise

   Rate your expertise (Expert/Strong/Developing) in: AWS, Azure, GCP. How many certified engineers per platform? List specific certifications held (Solutions Architect, Data Engineer, etc.).

10. B.2

    Data Warehouse Platforms

    Rate expertise in: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse. How many production deployments completed per platform? Average data volume handled?

11. B.3

    Data Engineering Tools

    Experience with: dbt, Airflow, Spark, Kafka, Fivetran, Airbyte. Provide specific project examples using each tool.

12. B.4

    Data Modeling Methodology

    What data modeling approaches do you use (Kimball, Data Vault, Inmon)? Experience with dimensional modeling, star schemas, and modern data mesh patterns?

13. B.5

    AI/ML Production Experience

    How many ML models have you deployed to production in the past 12 months? Provide 3 examples with: problem type, accuracy metrics, business impact, and current status (still running?).

14. B.6

    GenAI/LLM Capabilities

    Experience with LLM implementations: RAG architectures, fine-tuning, prompt engineering, vector databases. Any production GenAI deployments? What models used (GPT-4, Claude, Llama)?

15. B.7

    MLOps & Model Lifecycle

    MLOps tooling experience: MLflow, Kubeflow, SageMaker Pipelines, Vertex AI. How do you handle model versioning, monitoring, and retraining?

16. B.8

    Real-Time Data Processing

    Experience with streaming architectures: Kafka, Kinesis, Pub/Sub. Any production event-driven systems? Typical latency requirements met?

17. B.9

    Data Quality & Observability

    Tools used for data quality: Great Expectations, Monte Carlo, dbt tests. How do you implement data observability and lineage tracking?

18. B.10

    BI & Visualization

    Expertise in: Tableau, Power BI, Looker, Sigma Computing. Semantic layer implementation experience? Self-service analytics enablement?

19. B.11

    Migration Experience

    Experience migrating from: Oracle, Teradata, SQL Server, on-premise Hadoop. Typical migration timeline for [INSERT DATA VOLUME]? Schema conversion approach?

20. B.12

    Cost Optimization Expertise

    How do you optimize cloud and platform costs? Experience with FinOps practices? Typical cost savings achieved in platform implementations?

21. B.13

    DevOps & Infrastructure as Code

    IaC tooling: Terraform, CloudFormation, Pulumi. CI/CD pipeline setup for data workloads. Git workflow preferences?

22. B.14

    Programming Languages

    Primary languages used: Python, SQL, Scala, Java, R. Percentage of team proficient in each? Code quality standards enforced?

23. B.15

    Technical Debt Management

    How do you handle technical debt during implementations? Approach to balancing delivery speed vs. code quality? Documentation standards?

24. C.1

    Proposed Team Structure

    Provide detailed team composition: number of resources by role (PM, Architect, Senior Engineer, Junior Engineer), expected allocation percentage, and onshore/offshore split.

25. C.2

    Key Personnel Resumes

    Provide anonymized resumes for: Lead Architect, Project Manager, and 2-3 senior engineers. Include years of experience, relevant certifications, and similar project experience.

26. C.3

    Certification Count

    How many team members hold relevant certifications? List counts: AWS (Solutions Architect, Data Engineer), Snowflake (SnowPro Advanced), Databricks (Data Engineer), etc.

27. C.4

    Resource Continuity

    What is your typical resource turnover rate? How do you guarantee key personnel continuity? What happens if a critical resource leaves mid-project?

28. C.5

    Ramp-Up Time

    How quickly can you mobilize the proposed team? Typical ramp-up time from contract signing to full team productivity?

29. C.6

    Bench Strength

    Do you have bench resources available for rapid scaling? How many additional resources with similar skills could be added within 30 days?

30. C.7

    Subcontracting

    Will you subcontract any portion of this work? If yes, what percentage and what capabilities? How do you ensure quality control over subcontractors?

31. C.8

    Knowledge Transfer Plan

    How do you handle knowledge transfer to our internal team? Do you include training as part of the engagement? What documentation will be provided?

32. C.9

    Communication & Reporting

    Proposed communication cadence: daily standups, weekly status reports, steering committee meetings. Tools used for project management and collaboration?

33. C.10

    Escalation Process

    Describe your escalation matrix. How are technical blockers, budget concerns, and timeline risks communicated and resolved?

34. D.1

    Proposed Solution Architecture

    Provide a high-level architecture diagram for our use case. Explain technology choices and how they address our specific requirements.

35. D.2

    Project Phases & Timeline

    Break down the project into phases with estimated durations. Include: Discovery, Design, Development, Testing, Deployment, and Post-Go-Live support.

36. D.3

    Delivery Methodology

    Agile, Waterfall, or Hybrid? Sprint duration? How do you handle requirement changes mid-project? Change control process?

37. D.4

    Quality Assurance

    Testing strategy: unit tests, integration tests, performance tests, UAT. Code review process? Automated testing coverage targets?

38. D.5

    Risk Mitigation

    Identify top 5 project risks for our specific use case. What mitigation strategies will you employ? How are risks tracked and communicated?

39. D.6

    Data Migration Strategy

    If applicable: How will you handle data migration? Incremental or big-bang approach? Data validation and reconciliation process?

40. D.7

    Success Metrics

    How will you measure project success? Propose specific KPIs: performance benchmarks, adoption rates, cost targets, query response times.

41. D.8

    Go-Live Support

    What support will you provide during and immediately after go-live? Hypercare period duration? On-call support availability?

42. D.9

    Documentation Deliverables

    List all documentation to be provided: architecture docs, runbooks, data dictionaries, operational procedures, training materials.

43. D.10

    Continuous Improvement

    How will you identify and implement improvements during the project? Lessons learned process? Performance optimization recommendations?

44. D.11

    Intellectual Property

    Clarify IP ownership: Do we own all code, configurations, and models developed? Any pre-existing IP or accelerators being used? Licensing terms?

45. D.12

    Post-Project Support Options

    What ongoing support models do you offer? Managed services, retainer arrangements, break-fix support? Rates for post-project engagement?

46. E.1

    Total Cost Estimate

    Provide detailed cost breakdown: labor costs by role, platform/tool licensing, infrastructure estimates, travel expenses, and any third-party costs.

47. E.2

    Rate Card

    Hourly/daily rates for each role: Partner/Director, Senior Architect, Senior Engineer, Mid-level Engineer, Junior Engineer, Project Manager. Differentiate onshore vs. offshore if applicable.

48. E.3

    Pricing Model Preference

    Do you prefer Time & Materials, Fixed Price, or Hybrid? For each model, explain when you recommend it and associated risk/benefit trade-offs.

49. E.4

    Payment Terms

    Proposed payment schedule: milestone-based, monthly invoicing, upfront deposits? Standard payment terms (Net 30, Net 45)?

50. E.5

    Change Order Process

    How are scope changes handled? Change order approval process? Impact on timeline and budget for typical scope additions?

51. E.6

    Volume Discounts

    Are volume discounts available for extended engagements or additional project phases? What commitment levels trigger discounts?

52. E.7

    Warranty Period

    What warranty do you provide on delivered work? Duration? What's covered (bug fixes, performance issues, design flaws)?

53. E.8

    Liability & Insurance

    Professional liability insurance coverage amounts? Liability caps in your standard contract? Willingness to accept higher liability limits?

54. E.9

    Termination Clauses

    Standard termination terms: for cause, for convenience. Notice periods? Wind-down assistance? Data handover process?

55. E.10

    Cost Optimization Commitment

    Will you commit to cost optimization targets? Any gain-sharing arrangements for cost savings achieved? FinOps practices included?

56. F.1

    Security Certifications

    List security certifications held: SOC 2 Type II, ISO 27001, HITRUST, PCI DSS. Provide current audit reports or attestation letters.

57. F.2

    Data Handling Practices

    How will you handle our sensitive data during the engagement? Encryption in transit and at rest? Data residency requirements? Access controls?

58. F.3

    Regulatory Compliance Experience

    Experience with: GDPR, CCPA, HIPAA, SOX, PCI DSS. Provide examples of compliant implementations in each relevant framework.

59. F.4

    Background Checks

    Do you conduct background checks on all personnel? What level of screening (criminal, credit, education verification)? How recently updated?

60. F.5

    NDAs & Confidentiality

    Are you willing to sign our NDA? Any standard NDA terms you cannot accept? Confidentiality provisions for subcontractors?

61. F.6

    Disaster Recovery

    Business continuity plans if key personnel become unavailable? Backup procedures for critical project deliverables? RPO/RTO commitments?

62. F.7

    Audit Rights

    Will you grant us audit rights to review your security practices, code quality, and project progress? Frequency and scope of audits?

63. F.8

    Incident Response

    Security incident response plan? How quickly will we be notified of security breaches? Remediation process and timelines?