Companies that use Starburst

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

My analysis reveals that Starburst purchasing decisions are driven primarily by senior data and technology leadership, with Director of Information Technology (9%), Director of Product Management (7%), and Director of Data Engineering (7%) roles making up key decision-makers. These leaders are focused on modernizing data infrastructure, enabling self-service analytics, and building scalable data platforms. Their strategic priorities center on cloud migration, particularly to AWS and Azure, establishing data governance frameworks, and creating enterprise-wide data products that support AI and machine learning initiatives.

The day-to-day users are predominantly Data Engineers (19% of postings), who implement Starburst alongside Databricks, Snowflake, and cloud-native architectures. These practitioners build data pipelines, optimize query performance, manage data virtualization layers, and ensure data quality across distributed systems. I noticed many roles explicitly mention Starburst as part of the modern data stack, working with tools like dbt, Airflow, and Trino to enable analytics and reporting capabilities for business users, data scientists, and analysts.

The pain points are clear across these postings. Companies seek to eliminate data silos and achieve a single source of truth, with one role stating the need to reach "single-source of truth for each data element with key stakeholders." Another emphasizes "transparent financial markets" through better data access. A third highlights the goal of "provisioning secure, governed data access at the speed modern business demands." These organizations are tackling data fragmentation, governance challenges, and the need for real-time insights to support increasingly complex analytics and AI workloads.

Source: Analysis of Linkedin bios of 503 companies that use Starburst

I noticed that Starburst customers span an incredibly wide range of what they actually do. They include global financial institutions processing trillions in transactions (MUFG, State Street), retailers operating massive supply chains (Target, Flipkart), energy companies managing critical infrastructure (Talen Energy), healthcare organizations running clinical trials (Science 37, Freenome), and technology platforms building everything from payment systems to AI tools. What connects them is that they're all drowning in data across multiple systems and need to make sense of it at scale.

These are predominantly mature, established enterprises. I see numerous Fortune 500 companies, post-IPO businesses, and organizations with 1,000+ employees. Even the smaller companies tend to be well-funded (Series C and beyond) or backed by major investors. They're not scrappy startups experimenting with data. They're organizations with legacy systems, regulatory requirements, and massive scale challenges. The employee counts, funding stages, and global footprints all signal companies past the early growth phase.

Source: Analysis of tech stacks from 503 companies that use Starburst

I noticed that Starburst users have tech stacks that point strongly toward enterprise B2B companies with sophisticated go-to-market operations. The combination of sales enablement tools like Mindtickle, customer experience platforms like Qualtrics, and critical infrastructure monitoring through PagerDuty tells me these are mature companies focused on both acquiring and retaining large enterprise accounts.

The pairing of Mindtickle and Qualtrics is particularly revealing. Mindtickle helps sales teams learn complex products, which makes sense since Starburst itself is a technical data analytics platform. Companies need well-trained reps to sell it. Meanwhile, Qualtrics showing up so frequently suggests these companies obsess over customer satisfaction and likely have expansion revenue models where keeping customers happy drives growth. The presence of OneLogin, an enterprise identity management tool, reinforces that these companies serve large organizations with strict security requirements.

My analysis shows these are definitively sales-led organizations, likely in growth or mature stages rather than early startup phase. The SupportLogic correlation, even with fewer companies, is striking because it's a tool specifically for analyzing support interactions to prevent churn. This signals companies with substantial customer bases worth protecting. PagerDuty's strong presence indicates they're running always-on services where downtime has real business consequences. Golinks appearing frequently suggests distributed teams that need internal knowledge management, pointing to companies past the small startup phase.