Custom software and data engineering services for enterprise cloud migration
DVT is a 500+ person software services firm founded in South Africa in 1999, now operating at global scale. The tech stack reveals a mature, multi-cloud engineering operation: Python + Node.js backends, Angular/TypeScript frontends, Postgres/MongoDB data layers, and Docker containerization. Active adoption of infrastructure-as-code tools (Terraform, CloudFormation, AWS CDK) combined with a project list dominated by cloud migration, lakehouse architectures, and pipeline automation signals a shift toward modern data platforms — a pattern reflected in hiring velocity accelerating with senior engineering-heavy roles focused on data and infrastructure work.
DVT provides custom software development, AI, and data engineering services to enterprise clients globally, with a base in Johannesburg. The company operates as a BEE level 2 provider and maintains strong ties to South African clients while scaling internationally. Current engagement patterns show heavy concentration on data platform modernization: Standard Bank data platform work, SQL/SSIS pipeline development, ETL/ELT architecture, and migration from legacy systems to cloud-native lakehouse designs on AWS and Azure. The technical footprint spans full-stack development (Python, Node.js, Angular), cloud infrastructure provisioning, and CI/CD pipeline work, with organizational focus on senior and mid-level engineers tackling compliance, security, and automation challenges.
Python, Node.js, Angular, FastAPI, PostgreSQL, MongoDB, Docker, AWS, Azure. Stack also includes Django ORM, RabbitMQ, Redis for async/streaming, and SQL Server for enterprise clients.
Cloud data platform modernization: lakehouse architectures, data migrations from legacy systems, ETL/ELT pipeline development, infrastructure-as-code provisioning, and CI/CD automation. Current major engagements include Standard Bank data platform work.
DVT'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.