Nearshore software development and engineering services across the Americas
Coderio is a nearshore software outsourcing firm operating from New York with 201–500 employees across Peru, Brazil, Argentina, Uruguay, Colombia, and Ecuador. The tech stack spans full-stack languages (Go, Python, Java, Node.js), cloud infrastructure (AWS, Azure, Kubernetes, Docker), and a heavy data-science layer (NumPy, SciPy, Jupyter, Snowflake, dbt, Tableau, Power BI). Active hiring is overwhelmingly senior-focused (35 of 46 open roles), concentrated in engineering (36 roles), signaling aggressive delivery on complex projects—particularly legacy modernization (Golang migration, cloud-native refactoring) and healthcare data pipelines at scale.
Notable leadership hires: Tech Lead
Coderio delivers nearshore software development and managed engineering squads to mid-market and enterprise clients across North America. The company structures engagements from individual engineer augmentation to fully managed development teams, with promised 7-day deployment timelines. The project mix reflects a mix of greenfield work (SaaS platforms, API-first architectures, distributed systems on AWS) and significant modernization work (legacy Golang migration, cloud-native refactoring, healthcare data pipeline scaling). Pain points cluster around legacy system performance, distributed-team coordination under delivery pressure, and complex multi-layer system integration—all typical of firms handling both customer software delivery and internal platform evolution.
Go, Python, React, Java, Node.js, AWS, Azure, Docker, Kubernetes, Snowflake, dbt, and data science tools (NumPy, SciPy, Jupyter). Actively adopting OpenShift and Microsoft Fabric; replacing Go in legacy systems.
Legacy Golang migration, healthcare SaaS platforms powered by large-scale datasets, cloud-native modernization, distributed systems on AWS, supply chain data products, and API-first architecture rollouts.
Coderio'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.