Mobile attribution and marketing measurement platform for app businesses
AppsFlyer operates a data-heavy mobile marketing platform serving 15,000+ businesses with attribution, deep linking, and AI-driven workflows. The stack—Scala, Spark, Kafka, Kubernetes on AWS/GCP/Azure—reflects a company built for high-throughput, low-latency data pipelines; they're adopting GraphQL Federation, suggesting architectural evolution toward distributed query layers. Active hiring skews toward sales and support (44 of 71 roles) rather than engineering, paired with retention and churn challenges in their pain list—a pattern indicating shift from product-led scaling to customer success at scale.
Notable leadership hires: Software Group Lead
AppsFlyer is a mobile marketing platform that unifies attribution, measurement, and workflow automation across iOS and Android ecosystems. The company serves app marketers at mid-market and enterprise scale, integrating with app stores (Apple App Store, Google Play) and advertising networks. They operate globally with 1,001–5,000 employees, hiring across 14 countries spanning North America, Europe, Asia-Pacific, and the Middle East. Core technical challenges center on reliable data export, mobile attribution accuracy, and supporting high-throughput, low-latency distributed systems for their customer base.
Core languages: Scala, Python, Go, Java, Kotlin. Data layer: Apache Spark, Kafka, AWS RDS, Athena, Neo4j, Redis. Cloud: AWS, GCP, Azure. DevOps: Kubernetes, Docker. Analytics: Datadog, Looker. Adopting GraphQL Federation.
Yes. Engineering roles comprise 14 of 71 active openings (19%). Seniority mix is senior-weighted (33 of 71 total), and they're hiring across 14 countries including US, Israel, Poland, India, and Vietnam.
San Francisco, CA. The company operates globally with hiring in 14 countries across North America, Europe, Asia-Pacific, Southeast Asia, and the Middle East.
AppsFlyer'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.