Data accuracy and analytics platform for distributed intelligence systems
Tresata runs a Java/Spring + Kafka + Spark + Kubernetes stack built for distributed data processing and inference at scale. The tech shape—Hadoop, EMR, Dataproc across cloud providers—suggests a platform for handling large analytical workloads across AWS, Azure, and GCP. Active hiring is concentrated in engineering and support leadership, with projects focused on infrastructure automation (Terraform, Kubernetes) and monitoring product usage patterns, indicating a shift toward operational maturity and self-serve observability.
Notable leadership hires: Customer Support Director
Tresata builds a data-centric analytics platform designed to make advanced data processing and inference accessible to organizations of any size. Founded in 2011 and based in Charlotte, NC, the company operates as a small, engineering-focused team (11–50 employees). The platform is architected for distributed computing across major cloud providers and leverages open-source big-data tools (Spark, Hadoop, Kafka) alongside proprietary inference capabilities. Current focus areas include infrastructure automation, multi-cloud deployment, and improving customer support processes around SLA compliance and case resolution.
Java, Spring Boot, GraphQL, Kafka, Apache Spark, Hadoop, Kubernetes, PostgreSQL, MongoDB Atlas, Cassandra, and tooling across AWS, Azure, and GCP. Currently adopting Kubernetes for container orchestration.
Infrastructure automation (Terraform, Kubernetes deployment), product monitoring and usage analytics, customer support process improvement, and onboarding/training program development.
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