Companies that use Snowflake (filter by AWS, Azure or GCP)

Our data is enriched with the employee size, industry and the technologies each company runs on. Here are some examples of companies using Snowflake.

BlackRock

Asset Management - New York, NY

BlackRock is the world's largest asset manager, and much of the investment industry runs on its Aladdin platform. The data backbone under Aladdin, BlackRock's Enterprise Data Platform, leans on Snowflake.

Data engineers use Snowflake alongside Airflow, Python, and dbt to run the platform's ingestion, processing, orchestration, and data-quality frameworks. The heart of the job is automating pipelines that feed clean, reliable data into the systems investment teams depend on, with stored procedures and UDFs doing the heavy transformation.

When the platform serving trillions in assets has to stay accurate release after release, end-to-end testing on every deploy is what keeps it trustworthy.

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Principal Financial Group

Financial Services - Des Moines, IA

Principal is a retirement and asset-management firm, and it builds its data platform on Snowflake wired tightly to AWS.

Engineers lean on Snowflake's native machinery: Snowpipe for continuous loading, Streams and Tasks for change tracking and scheduling, and stored procedures for transformation, sitting alongside S3, Lambda, and KMS. They also use Snowflake's data sharing to move governed data without copying it around.

The emphasis is disciplined delivery: readable, maintainable pipeline code built to coding and data-protection standards, shipped through CI/CD in GitHub. For a company holding people's retirement savings, that mix of speed and control is the point.

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Commonwealth Bank

Banking - Sydney, Australia

Commonwealth Bank runs one of the largest data-engineering operations in Australia, and Snowflake is a pillar of it.

The team designs and builds ETL frameworks on cloud-native technologies like AWS and Snowflake, and a big slice of that work sits in the bank's regulatory reporting crew. That means Snowflake data products underpinning regulatory capital, climate disclosures, and compliance reporting, the numbers a bank has to get exactly right for its regulators.

The engineering bar is high: hands-on design, build, and optimization of the data products, continuously improved as the rules change.

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Wise

Fintech - London, UK

Wise moves money across borders for millions of customers, and behind its FX pricing sits a serious data operation.

Its Treasury Markets Data Science team uses Snowflake as the warehouse under the production infrastructure for FX pricing, risk, and trading. Quantitative developers work directly against that data to price currency, manage risk, and run the trading systems that keep Wise's fees low and its transfers fast.

When the data feeding a live pricing engine has to be both fast and correct, the warehouse choice matters.

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Publix Super Markets

Grocery Retail - Lakeland, FL

Publix is the largest employee-owned company in the US, with a 2,300-person technology team serving more than 1,400 stores across eight states.

Its data architects build the enterprise data warehouse on Snowflake, defining the standards and models the rest of the business builds on. The work runs deep into warehouse methodology, star schemas, dimensional modeling, ETL, with large-scale pipelines orchestrated by tools like Apache Airflow.

It's the data foundation behind everything from inventory to analytics for a grocer that size.

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American Airlines

Airline - Fort Worth, TX

American Airlines runs one of the world's largest flight operations, and Snowflake is part of the data platform its IT teams build on.

Alongside Teradata, Cassandra, and a deep Azure stack, Data Factory, Databricks, Data Lake, developers use Snowflake as one of the airline's core data platforms. The engineering work ties Python, Spark, and Azure services together to move and shape the data behind applications across the airline.

At an operation this size, that data plumbing quietly touches scheduling, operations, and everything in between.

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Boeing

Aerospace - Arlington, VA

Boeing's approach to Snowflake is distinctive: it treats the platform as infrastructure to be engineered, not just used.

Senior DevOps developers build reusable infrastructure-as-code modules that deploy Snowflake, alongside Databricks and Kubernetes, across AWS and Azure. They wire those deployments into CI/CD pipelines so data platforms can be stood up reliably and repeatably.

It's Snowflake seen through an infrastructure lens: secure, highly available, and provisioned the same disciplined way as the rest of Boeing's cloud estate.

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TransUnion

Credit & Risk Data - Chicago, IL

TransUnion is a credit bureau, a business that lives or dies on moving data accurately at scale, and Snowflake sits in that machinery.

Its data-management platform teams use Snowflake within data-lake and warehouse architectures to ingest, validate, monitor, and transform data across the enterprise, batch, online, and streaming. Paired with tools like Kafka, Flink, Iceberg, and dbt, the goal is onboarding workflows that let teams bring in and trust new data fast.

For a company whose product is data, that ingestion layer is close to the core.

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Equifax

Credit & Risk Data - Atlanta, GA

Equifax, another of the big credit bureaus, uses Snowflake as one of its cloud data lakes for building and optimizing machine-learning data pipelines.

Its data engineers sit at the bridge between data engineering and business intelligence, turning raw data into models and dashboards that drive decisions. Advanced SQL over Snowflake feeds the analytics, and visualization tools like Looker carry the insights the rest of the way.

It's Snowflake doing double duty, the pipeline substrate and the analytics source, at a data-native company.

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Verisk

Insurance Analytics - Jersey City, NJ

Verisk builds the analytics that help the world's insurers price and assess risk, and Snowflake is part of its modern application stack.

Engineers pair Snowflake with .NET, Python, REST APIs, and microservices, running it all in cloud environments like AWS to power risk products. Alongside search technologies like OpenSearch and Elasticsearch, Snowflake is where a lot of the data-heavy analytical work lives.

For a company selling risk intelligence, the data platform is the product's foundation.

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BD (Becton Dickinson)

Medical Technology - Franklin Lakes, NJ

BD is one of the largest medical-technology companies in the world, producing billions of MedTech products a year, and its commercial teams run on data sitting in Snowflake.

Analytics engineers build the dashboards, reports, and semantic-layer assets that let business users pull governed, accurate insights for themselves. Snowflake is the modern cloud data platform underneath that self-service layer, feeding the BI tools and data products the commercial organization relies on.

The emphasis is governance, making sure the numbers people self-serve are the right ones.

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Crocs

Retail Apparel - Broomfield, CO

Crocs runs its digital and direct-to-consumer analytics on data warehoused in Snowflake.

Its analytics team designs automated data pipelines and self-service reporting frameworks on top of it, then layers AI-driven insights over the top for the DTC, marketing, and onsite teams. Snowflake is the warehouse feeding BI tools like Power BI and Tableau, plus web-analytics data from Adobe and GA4.

The aim is faster, more proactive decisions about how the brand sells online.

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Source: Analysis of job postings that mention Snowflake (using the Bloomberry Jobs API)

I analyzed these postings and found that Snowflake purchasing decisions span a wide range of leadership roles. Half the postings (50%) are for leadership positions including directors of data engineering and management (13% combined), VPs of data architecture and enterprise data (12% combined), and heads of marketing and compensation at Snowflake itself. These buyers are driving strategic priorities around AI readiness, data governance, and enterprise-wide data transformation. They're building teams to move organizations from reactive reporting to proactive, AI-ready analytics models.

The day-to-day users are primarily data engineers (14% of postings) and analytics engineers who build pipelines, manage data quality, and create semantic layers. These practitioners work across the modern data stack including dbt, Fivetran, and various cloud platforms. They're building scalable data warehouses, implementing data contracts, and creating governed data products that business users can trust. The technical work spans ETL/ELT development, performance optimization, and integration with ML platforms.

The pain points center on breaking down data silos and enabling AI at scale. Companies repeatedly mention transforming to become AI-ready, with phrases like "AI-ready platforms with appropriate semantic and data access layers" and moving from "reactive, dashboard-centric reporting to a proactive, AI-ready analytics model." Another theme is data governance and trust, with organizations seeking "governed, scalable, and trusted analytics" and building "reliable, high-quality data pipelines." The shift from fragmented legacy systems to unified, cloud-native platforms emerges as a core driver.

Source: Analysis of tech stacks from 1,361 companies that use Snowflake

I noticed that companies using Snowflake on major cloud platforms are predominantly enterprise-scale organizations with sophisticated go-to-market operations and a strong emphasis on both employee experience and customer data management. The presence of tools like Workday, Okta, and Qualtrics signals these are mature companies with significant headcount, likely 500+ employees, that have invested heavily in enterprise infrastructure.

The pairing of Snowflake with Adobe Audience Manager and Watershed is particularly revealing. These companies are collecting massive amounts of customer data, using Snowflake as their central warehouse, then activating that data through Adobe's marketing platform while measuring actual business outcomes through Watershed. The inclusion of Mindtickle, a sales enablement platform, suggests they're running complex B2B sales motions that require continuous training and coaching. This isn't a simple product-led growth model but rather a coordinated effort where sales teams need deep product knowledge and the company tracks detailed attribution data.

My analysis shows these are definitively sales-led organizations, likely in late growth or mature stages. The combination of Okta for identity management, Workday for HR operations, and sophisticated sales enablement tools indicates companies that have moved past startup chaos into structured operations. They're investing in understanding customer sentiment through Qualtrics while simultaneously building robust data infrastructure, which tells me they're focused on retention and expansion, not just new customer acquisition.