Enterprise modernization and data engineering services across Europe and Latin America
Sngular is a 1,300+ person consulting firm executing large-scale cloud migrations and data platform rebuilds. The tech stack—Node.js, Spring Boot, Kafka, PostgreSQL, and Oracle-to-GCP transitions—reflects a services business anchored in backend modernization. Active adoption of LangChain and LangGraph signals a recent pivot toward AI-powered services; the project list (legacy-to-cloud, data decoupling from Oracle, MLOps pipelines) and pain-point clustering (data governance, reliable deployments, scalable architecture) confirm their near-client teams are deep in multi-year transformation engagements.
Notable leadership hires: Tech Lead
Sngular provides software development, data science, and cloud modernization services to enterprise clients across Europe and Latin America. Founded in 2014, the company operates 20 global offices and serves over 500 customers. The delivery model centers on near-client placement of engineering teams (spanning senior backend engineers, data architects, and MLOps specialists) who own legacy-to-cloud migrations, data platform redesigns, and automation initiatives. Primary technical domains include cloud infrastructure (GCP, Atlassian tooling), streaming and event-driven architecture (Kafka, RabbitMQ), and emerging AI features (LLMs via LangChain). Hiring velocity is accelerating across Spain, Mexico, and Portugal, with engineering roles dominating the open headcount.
Core stack: JavaScript, Node.js, Angular, TypeScript (frontend); Java, Spring Boot, Spring Cloud (backend); PostgreSQL, MongoDB, Kafka, RabbitMQ (data/messaging); PyTorch, TensorFlow, GPT, Claude, Mistral (AI/ML). Recently adopting LangChain and LangGraph; migrating away from Oracle.
Primary projects: legacy-to-cloud migrations, Oracle-to-GCP data decoupling, MLOps pipelines for forecasting, Power BI framework implementation, and Atlassian marketplace application development. Recurring themes: data architecture design, incident resolution, and modernizing legacy data platforms.
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