Digital audio and podcast platform with measurement and monetization tools
Triton Digital operates a multi-layered platform serving broadcasters, podcasters, and online audio services across 80+ countries. The tech stack reveals a data-intensive operation: Kafka and Kafka Streams for streaming, Apache Spark and PySpark for batch processing, Druid for metrics queries, and Looker for analytics—all pointing toward real-time audience measurement at scale. Current projects show aggressive AI adoption (RAG pipelines, agentic systems, chatbot backends) alongside infrastructure modernization (Databricks deployment, platform migration, CI/CD automation for ML), while pain points cluster around terabyte-scale query optimization and production-readiness of AI models.
Triton Digital is a software company serving the digital audio, podcast, and broadcast radio sectors. The platform spans three operational areas: audience and advertising measurement (via Webcast Metrics and Podcast Metrics services), revenue optimization tools for broadcasters and podcasters, and the operational infrastructure that powers online audio services globally. The company operates from New York with 201–500 employees and maintains hiring activity in the United States, Canada, and Qatar. Data engineering and core engineering roles dominate the hiring mix, reflecting the data pipeline and backend complexity required to process and measure streaming audio at global scale.
Triton uses Kafka and Kafka Streams for streaming, Apache Spark and PySpark for batch processing, Apache Druid for metrics queries, Looker for analytics, Kubernetes and Docker for orchestration, and AWS, GCP, and Azure for cloud infrastructure. LangChain and LangGraph support emerging AI workloads.
Core projects include audience and advertising measurement platforms, revenue optimization features, terabyte-scale data pipeline improvements, AI chatbot and RAG pipeline development, and migration of legacy systems to modern infrastructure (Databricks, Kubernetes, ArgoCD).
Triton Digital'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.