Companies that use Dagster

Source: Analysis of 100 job postings that mention Dagster

I noticed that Dagster buyers are predominantly data engineering leaders, with Directors of Data Engineering (16%), Directors of Data Management (7%), and Heads of Data Engineering (6%) driving purchasing decisions. These leaders are focused on platform modernization, scaling data infrastructure, and enabling self-service analytics across their organizations. Their strategic priorities center on building reliable, production-grade data platforms that can support both traditional analytics and emerging AI/ML workloads while maintaining strong governance and data quality standards.

The day-to-day users are primarily Data Engineers (29% of postings), who leverage Dagster for orchestrating ETL/ELT pipelines, managing data transformations alongside tools like dbt, and ensuring data quality across diverse sources. I found that practitioners are working with cloud data warehouses like Snowflake, BigQuery, and Databricks, building pipelines that support everything from operational reporting to real-time analytics and machine learning model training.

The job postings reveal companies are tackling significant infrastructure challenges. Multiple roles emphasize the need to move beyond "managed services to a robust, self-hosted stack" and build platforms that are "scalable, reliable, and secure." Companies seek engineers who can "design and implement a generalised, self-service data sharing framework" and create "high-speed rails for financial data with zero-touch automation." The recurring theme is transitioning from fragmented, manual processes to unified, automated data platforms that can scale with growing business demands.

Source: Analysis of tech stacks from 1,402 companies that use Dagster

I noticed that companies using Dagster are consistently modern, security-conscious B2B organizations with sophisticated operational infrastructure. The overwhelming presence of enterprise identity management tools like Okta and OneLogin, combined with incident management through PagerDuty, tells me these are companies running critical data infrastructure that directly impacts their business operations and likely their customer commitments.

The pairing of Dagster with PagerDuty is particularly revealing. When your data pipelines are important enough to warrant 24/7 incident response, you're either processing data that directly feeds customer-facing products or you're running data operations as a service for others. The extreme correlation with Rocketlane, a customer onboarding platform, suggests many of these companies are in the latter category. They're likely B2B data or analytics platforms where customer implementations involve complex technical onboarding. Meanwhile, Golinks appearing so frequently indicates these are collaborative engineering teams that need to quickly reference internal documentation and resources, pointing to larger technical organizations with established internal tooling practices.

The full stack paints a picture of sales-led B2B companies in growth stage, likely Series B and beyond. The enterprise security tools indicate they're selling to regulated industries or large enterprises with strict compliance requirements. Docker Hub's presence confirms these teams are deploying containerized applications at scale. The combination of customer success tooling (Rocketlane) with enterprise security and incident management suggests companies that have found product-market fit and are scaling their go-to-market motion while maintaining high operational standards.

Source: Analysis of Linkedin bios of 1,402 companies that use Dagster

I noticed that Dagster users span a remarkably wide range, but they share a common thread: they're dealing with significant data complexity. These companies are building data-intensive products and platforms. Software development firms dominate, particularly those in data infrastructure, AI/ML, customer data platforms, and financial services. Companies like Amperity explicitly describe themselves as transforming "raw customer data into strategic business assets," while Revelio Labs is "absorbing and standardizing billions of public employment records." Even non-tech companies like kWh Analytics mention their "proprietary database of 300,000+ zero-carbon projects and $100B in loss data."

The maturity level varies dramatically. I see seed-stage startups like Genesis AI ($105M seed round) alongside post-IPO giants like Meta and BNY. However, there's a concentration in the Series A to Series C range, suggesting Dagster appeals to companies that have found product-market fit and are now scaling their data operations. Employee counts cluster around 50-500, though notable exceptions exist on both ends.