Cross-channel TV and streaming measurement platform for media buyers and publishers
VideoAmp unifies audience measurement across broadcast TV, cable, streaming, and digital—a data stack (Snowflake, Spark, Databricks, Python) built for multi-channel attribution at scale. The hiring mix (data-heavy with concurrent sales expansion) and active projects (attribution models, local station workflows, cross-screen viewership) signal a shift from reporting to prescriptive optimization, while pain points around low client adoption and complex deal cycles suggest the core challenge is operationalizing insights across fragmented media ecosystems.
VideoAmp is a software and data company that addresses the measurement gap in media buying and selling. The platform unifies audiences across traditionally siloed systems—broadcast TV, cable, streaming, and digital—giving advertisers, publishers, and agencies a unified view of campaign performance. Founded in 2014 and based in Los Angeles, the company operates in the 201–500 employee range and sells into mid-market to enterprise media buyers, publishers, and independent agencies. The product stack centers on data modeling (Snowflake, Spark, Databricks) and serves use cases around attribution, lift measurement, and cross-screen viewership.
VideoAmp uses Snowflake, Apache Spark, Databricks, Python, AWS, GCP, Kubernetes, PostgreSQL, and Tableau. Infrastructure tooling includes Terraform, ArgoCD, and Docker. They also integrate Salesforce, WideOrbit, and IronClad.
VideoAmp is actively hiring in the United States, Canada, and Peru.
VideoAmp's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.