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Top Data Engineering Companies

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TL;DR - Top 3 Data Engineering Companies

Industry-leading partners covering 90% of enterprise data platform use cases

1. phData

Score: 8.7/10 • $150-250/hr • 600-700 team

Best for: Snowflake migrations & modern data platforms. 6th consecutive Snowflake Partner of the Year (2025) with nearly 600 certifications and 98% client renewal rate.

Visit phData →

2. Tiger Analytics

Score: 8.6/10 • $75-150/hr • 5,000-6,000 team

Best for: AI/ML projects with data engineering foundations. Combines data engineering with advanced AI/ML capabilities. Full-lifecycle expertise from pipelines to production ML.

Visit Tiger Analytics →

3. STX Next

Score: 8.0/10 • $100-150/hr • 500+ team

Best for: Production-ready platforms & governance-focused solutions. Battle-tested across 200+ data projects with ISO 27001/9001 certification. Trusted by Mastercard & Decathlon.

Visit STX Next →

Snowflake vs Databricks

Head-to-head comparison with pricing, use cases, and partner recommendations

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RFP Checklist

50+ evaluation criteria for vendor selection with progress tracking

Complete Comparison

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Rank Firm Score Rate/hr Team Size Primary Platforms Best For Website

Top 10 Detailed Profiles

Quick Selection Framework

By Budget

$100-300K: Analytics8, STX Next, InterWorks
$300K-1M: phData, Sigmoid, Tiger Analytics
$1M+: Slalom, Accenture, Deloitte

By Platform

Snowflake: phData, Analytics8
Databricks: Lovelytics, Perficient
Multi-platform: STX Next, InterWorks

By Priority

Best Value: Sigmoid, N-iX
Speed: Tredence, phData
Innovation: Lovelytics, Tiger Analytics

By Industry

Retail/CPG: Tredence, Sigmoid
Finance: Analytics8, phData, STX Next
Healthcare: phData, Lovelytics

Decision Checklist

Rating Methodology

Data Sources: Gartner, Forrester, Everest Group reports; Clutch & G2 reviews (10+ verified reviews required); Official partner directories (Databricks, Snowflake, AWS, Azure, GCP); Company disclosures; Independent market rate surveys

Last Verified: November 16, 2025 | Next Update: January 2026

Technical Expertise

20%

Platform partnerships, certifications, modern tools (Databricks, Snowflake, dbt, streaming)

Delivery Quality

20%

On-time track record, proven methodologies, client testimonials, case results

Industry Experience

15%

Years in business, completed projects, client diversity, sector expertise

Cost-Effectiveness

15%

Value for money, transparent pricing, competitive rates vs capabilities

Scalability

10%

Team size, global reach, project capacity, resource ramp-up speed

Market Focus

10%

Ability to serve startups, SMEs, and enterprise clients effectively

Innovation

5%

Cutting-edge tech adoption, AI/ML capabilities, GenAI integration

Support Quality

5%

Responsiveness, communication clarity, post-implementation support

Red Flags to Avoid

Watch for these warning signs during vendor selection

🚩 Vague Proposals

Templated responses without demonstrating understanding of your environment

🚩 Unrealistic Promises

Dramatically faster or cheaper than benchmarks without justification

🚩 No Relevant Experience

Can't provide case studies from similar industries or company sizes

🚩 Poor Sales Communication

If responsiveness lacks during sales, expect worse during delivery

🚩 Resists References

Unwilling to connect you with current clients or past customers

🚩 Rigid Engagement Models

Inflexibility around scope changes or communication structures