Data engineering and cloud consulting for enterprise modernisation
Amber Labs is a data and cloud consulting firm built around Java, Python, and AWS infrastructure — with active adoption of additional AWS services signaling deeper platform entrenchment. The hiring mix is heavily engineering-skewed (40 of 56 roles), concentrated at senior and lead levels, suggesting they staff large transformation programmes rather than scale-up customer success. Current project load (data centre upgrades, microservices refactoring, government programme delivery, Maximo implementations) reflects enterprise-grade complexity work.
Notable leadership hires: Account Director
Amber Labs delivers data engineering, cloud migration, and analytics consulting to enterprise and public-sector clients. Founded in 2020 and based in London, the firm operates as a partnership structure with ~51–200 employees, predominantly technical staff. They specialise in designing and deploying cloud-native platforms (AWS, Azure), modernising legacy systems into microservices architectures, and implementing enterprise asset management solutions (IBM Maximo). Work spans data centre relocation, backend infrastructure scaling, and programme delivery for high-profile government and commercial clients. The firm invests in internal accelerators and delivery methodology to compress implementation timelines.
Primary languages: Java, Python, Node.js. Cloud: AWS and Azure. Data layer: PostgreSQL, Redshift, Athena, AWS Glue, PySpark, AWS EMR. Orchestration: Kubernetes, Docker, Helm, Argo CD, Concourse. Frontend: React, TypeScript. Infrastructure-as-code: Terraform.
Current projects include data centre relocation and upgrade, microservices architecture modernisation, central government programme delivery, IBM Maximo asset and waste management implementations, and backend services scaling for critical platforms.
Amber Labs'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.