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Salary
≈ $17k – $39k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Sep 30, 2026. DataArt scores B on the Alion truth index.

Overview
Company
Impact
Profile match
DataArt is a global software engineering and consulting firm headquartered in New York that builds and modernises custom software, data platforms and cloud infrastructure for clients in finance, travel, healthcare, media and retail. Founded in 1997, it runs delivery centres across Europe, the Americas and Asia, including offices in Wroclaw, Lublin and Lodz in Poland and teams in India, and works with more than 400 enterprise clients. Its openings include senior .NET, Java, Python and frontend engineers, DevOps and site reliability engineers, data architects and data engineers, QA automation leads and solution consultants.

We are looking for an experienced Platform Engineer to support the existing AWS infrastructure and contribute to its ongoing modernization and consolidation.

The role combines hands-on cloud engineering with a product mindset. We are not looking only for someone who can build or operate infrastructure - we need an engineer who treats the platform as a product and product engineering teams as customers: starting from developer friction rather than a preferred tool, building self-service and golden paths, and proving impact with data.

The working schedule is aligned with UK business hours, from 9:00 AM to 5:30 PM UK time. Participation in an on-call rota may be required.

This position includes a sign-in bonus.

You will contribute to a transformation from team operated infrastructure toward a platform with clear self service capabilities. The platform is run as an internal product: product engineering teams are its customers, and success is measured by developer experience, adoption, reliability, delivery speed and cost.

  • Design and evolve reliable AWS, Kubernetes, Terraform and CI/CD platform capabilities for engineering teams.
  • Understand developer friction through conversations, feedback and data before choosing a technical solution.
  • Build self-service capabilities, golden paths and guardrails that make the right thing easy for engineers.
  • Measure outcomes (e.g. lead time, deployment frequency, reliability, cost) to prioritize work and confirm improvements.
  • Work with product engineering teams to iterate on solutions and drive platform adoption.
  • Balance standardization and controls with usability, so the platform enables teams rather than adding process.
  • Strengthen observability, resilience, security and incident readiness.
  • Take part in incident response, post-incident reviews and the on-call rota.
  • Strong hands on AWS platform engineering experience in production environments.
  • Practical experience with Kubernetes, EKS, Docker, Terraform, and Helm.
  • Strong CI/CD experience, particularly with GitHub Actions and reusable pipeline design.
  • Experience with logging and monitoring, networking and protocols, secrets management, and service discovery.
  • Experience with at least one programming or scripting language.
  • Evidence of designing secure, reliable, and maintainable platform solutions.
  • Experience supporting business critical services, including participation in an on call rota.
  • Experience identifying developer needs and translating them into practical platform improvements.
  • Experience building self service capabilities, reusable workflows, or guardrails adopted by engineering teams.
  • Ability to use data and metrics to prioritize platform improvements and evaluate their impact on developer experience, reliability, delivery speed, or cost.
  • Experience collaborating with engineering teams to gather feedback, iterate on solutions, and drive adoption.
  • Ability to balance standardization, security, and operational controls with developer usability.
  • Experience with Honeycomb, OpenTelemetry, Prometheus or Splunk, including SLO-driven practices.
  • Experience using platform engineering metrics to measure adoption, developer experience and operational outcomes.
  • Pragmatic use of AI-assisted engineering tools to improve quality and productivity.
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