Snowflake Partners vs. Databricks Partners: Who Should You Hire in 2025?

The “Data Wars” are over. The two giants, Snowflake and Databricks, have both won. But for a buyer, this makes the implementation partner landscape confusing.

• Do you hire a “Snowflake Elite” partner to build your Lakehouse?
• Do you hire a “Databricks Champion” to manage your SQL warehousing?

This guide breaks down the nuances of the partner ecosystems, specifically for leaders hiring in 2025.

1. The Ecosystems Compared

It’s important to understand that the partners mirror the platforms they support.

The Snowflake Partner Persona

Snowflake sells “The Data Cloud”—an appliance-like experience that just works.

• The Vibe: Corporate, Polished, SQL-centric.
• Typical Partner Profile: Focuses heavily on dbt, Fivetran, and Tableau/Looker. They are “Modern Data Stack” integrators.
• Key Skillset: SQL optimization, Role-Based Access Control (RBAC), Data Governance, and Data Sharing.

The Databricks Partner Persona

Databricks sells “The Data Intelligence Platform”—a toolkit for engineering and AI.

• The Vibe: Engineering-first, Open Source, Python/Scala-centric.
• Typical Partner Profile: Focuses on Spark, Airflow, MLflow, and Unity Catalog. They often come from a Big Data / Hadoop background.
• Key Skillset: Distributed computing, Python, Machine Learning engineering, CI/CD for data.

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2. Certification Tiers: Filtering the Noise

Both providers have rigorous tiers. Don’t just look for a logo on a website; look for the tier.

Snowflake Partner Tiers to Watch

1. Elite (Top Tier): These firms have delivered 100+ successful projects and have dozens of certified “SnowPro” architects.
   • Examples: phData, Slalom, Deloitte, Accenture.
   • When to hire: Large-scale migrations, complex data sharing networks.
2. Premier (Mid Tier): Proven delivery capability, good for specific projects.
   • Examples: Analytics8, Hashmap (NTT), Hakkoda.
   • When to hire: Mid-market builds, specific dbt+Snowflake implementations.
3. Select (Entry Tier): Newer partners. Can be good value, but verify references heavily.

Databricks Partner Tiers to Watch

1. Global Consulting Partners: The massive GSIs.
2. Breadth vs Niche: Databricks awards “Brickbuilder” badges for specific industry solutions (e.g., “Brickbuilder for Mfg”).
   • Pro Tip: Look for the “Delivery Partner of the Year” awards. These are competitive signals of actual customer success, not just sales volume.

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3. The 2025 “Hybrid” Trend (Iceberg)

A major shift is happening in 2025: Apache Iceberg.

With Iceberg, data is stored in open formats (S3/ADLS) that both Snowflake and Databricks can read. This changes who you should hire.

• Old World: Hire a partner to “move data into Snowflake” (proprietary format).
• New World (2025): Hire a partner to “build an open Data Lakehouse” that Snowflake serves (BI) and Databricks processes (AI).

Recommendation: Ask prospective partners: “What is your strategy for Apache Iceberg and interoperability?” comparison.

• If they say “What?”, Run.
• If they explain a unified storage layer strategy, Hire.

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4. Cost Implications of Reference Architecture

Partners often bring their own “Reference Architectures” (templates). This impacts your long-term bill.

Expense Risk: The Snowflake Partner

• Risk: Some partners optimize for speed by writing inefficient SQL that scans terabytes of data. This looks great on Day 1 but blows up your credit consumption on Day 90.
• Audit Question: “How do you optimize for credit consumption? Do you implement resource monitors by default?”

Expense Risk: The Databricks Partner

• Risk: They might over-engineer a solution using complex Spark clusters that require high-maintenance DevOps, when a simple SQL Warehouse would have sufficed.
• Audit Question: “Do you use Serverless SQL for simple jobs, or do we need to manage cluster policies?“

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5. Decision Matrix

Requirement	Lean Towards…	Why?
Self-Service BI for extensive user base	Snowflake Partner	Snowflake’s multi-cluster warehousing concurrency is still the gold standard for high-user BI.
Complex Unstructured Data (Audio/Video)	Databricks Partner	Databricks native support for unstructured data in Delta tables is superior.
Data Sharing with External Vendors	Snowflake Partner	Snowflake’s “Data Sharing” feature is the most mature B2B data exchange method.
Heavy Python/ML Workloads	Databricks Partner	The notebook experience and MLflow integration are native home turf for Data Scientists.

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Conclusion: It comes down to “DNA”

When interviewing partners, try to sense their “Engineering DNA.”

• Snowflake Partners act like Analytics Engineers. They care about clean models, reliable dashboards, and business logic.
• Databricks Partners act like Software Engineers. They care about pipelines, latency, code abstraction, and adaptability.

You probably need both. But start with the one that solves your biggest immediate fire.

* **Data Sharing is critical:** You want to securely share live data tables with suppliers or customers. * **Team skillset:** Your internal team is strongest in SQL.

Top Snowflake Partners to Watch:

• phData: Renowned for massive-scale migrations and automation.
• Infostrux: Pure-play Snowflake experts focused on Data Vault.

The DNA of a Databricks Partner

Databricks partners often come from a Big Data, engineering, or Data Science background. They are comfortable with Spark, Python, Scala, and complex orchestration.

You should prioritize a Databricks specialist if:

• Your primary goal is AI/ML: You need to build recommendation engines, predictive models, or LLM applications.
• You have massive unstructured data: You are processing logs, images, or sensor data (IoT).
• You need complex streaming: Low-latency stream processing is a core requirement.
• Team skillset: Your internal team prefers Python/Spark code over comprehensive SQL.

Top Databricks Partners to Watch:

• Lovelytics: Strong focus on AI and high-impact visualization.
• Dataroots: European leaders in MLOps and AI engineering.
• Hakkoda: While strong in Snowflake, they are rapidly building Databricks capabilities for healthcare/fintech.

The Convergence: Hybrid Partners

In 2025, the best strategy for many enterprises is to find a partner who is excellent at both. The “Modern Data Stack” often involves using tools together—for example, using dbt for transformation on top of either platform.

Firms like Analytics8, phData, and Slalom have built substantial practices for both platforms. This allows them to offer truly “platform-agnostic” advice. If you aren’t 100% committed to one platform yet, hiring a hybrid partner for a paid Strategy/Discovery phase is often the safest investment.

Comparison Summary

Feature	Snowflake Specialist	Databricks Specialist
Core Heritage	Data Warehousing, SQL, BI	Big Data, Spark, AI/ML
Typical Project	EDW Migration, Self-Service BI	Lakehouse build, MLOps, Streaming
Key Tech Stack	dbt, Fivetran, Tableau	Spark, Airflow, MLflow, Delta Lake
Cost Model focus	optimizing credit consumption (Compute)	optimizing cluster utilization (Compute + Storage)
Best For	Democratizing data access	Engineering complex data products

Conclusion

Don’t just hire a “data partner.” Hire a partner whose DNA matches your destination.

• Going SQL-first? Look for Snowflake Elites.
• Going Code-first (AI/ML)? Look for Databricks Brickbuilders.
• Unsure? Hire a hybrid firm to do a Proof of Concept (PoC) on both.

Ready to find your match? Use our Partner Matching Tool to get a shortlist of verified firms for your specific needs.