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Salary
≈ $26k – $70k per year (Estimated)
Location
Remote (Brazil, Colombia, Uruguay)
Seniority
Architect
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 23, 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 a Solution Architect to help translate business goals into scalable, secure, and maintainable technical solutions. You will work across product discussions, architecture design, technical reviews, and cross team collaboration to support clear requirements and consistent engineering practices.

This project is centered on a modern travel focused platform with cloud native services, distributed systems, data integrations, and observability. It aims to improve productivity, quality, security, reliability, and time to market through consistent architecture principles and implementation ready technical solutions.

  • Define and communicate target architecture aligned with business objectives and product strategy
  • Design scalable and secure solutions across cloud native applications APIs data platforms integrations and distributed systems
  • Guide the adoption of Azure services containerized workloads event driven architecture Snowflake and Datadog
  • Evaluate architectural alternatives and document trade offs risks dependencies and recommendations
  • Identify opportunities to simplify systems reduce technical debt and improve platform reusability
  • Partner with stakeholders to write review validate and improve product and technical requirements
  • Translate business needs into clear testable and technically feasible requirements
  • Identify gaps assumptions dependencies integration constraints and architectural risks before development starts
  • Help teams define acceptance criteria API contracts data requirements system boundaries and testing strategies
  • Guide engineering teams in adopting architectural directions standards reusable patterns and decision records
  • Review proposed designs and significant technical changes and provide timely feedback
  • Help identify and manage architectural dependencies across teams products platforms and shared services
  • Drive responsible adoption of AI capabilities across the software development lifecycle
  • Evaluate AI enabled tools frameworks and platforms based on value security reliability maintainability and cost
  • Define architectural patterns for integrating AI services into applications
  • Establish guidelines for AI generated code content validation data protection and secure AI tool usage
  • Promote AI assisted testing including test generation regression coverage API validation and failure analysis
  • Measure engineering and delivery outcomes using relevant performance and quality indicators
  • Lead by example with clean architecture secure coding effective testing and maintainable implementation practices
  • Help teams improve CI/CD pipelines automated testing deployment strategies infrastructure as code and delivery reliability
  • Promote domain driven design modularity API first design asynchronous communication and event driven integration
  • Collaborate with engineering managers technical leads developers QA engineers data engineers and product teams to resolve technical challenges
  • Mentor technical leaders and engineers in architecture skills decision making and engineering ownership
  • Facilitate architecture reviews technical design workshops knowledge sharing sessions and cross team discussions
  • Present architecture changes engineering practices frameworks and technology investments to technical and non technical stakeholders
  • Guide the design of reliable integrations across internal systems third party providers booking platforms and external services
  • Establish patterns for event driven communication API integration messaging data consistency retries idempotency and failure recovery
  • Collaborate with data teams on Snowflake architecture data ingestion transformation governance quality and analytical access patterns
  • Promote observability through metrics logs distributed tracing dashboards and alerts using Datadog
  • Apply controls for sensitive data access management secrets compliance and secure information handling
  • Experience in software engineering and solution architecture designing and delivering production systems
  • Understanding of distributed systems cloud native architecture microservices modular monoliths API design and system integration
  • Ability to translate business requirements into technical designs and implementation plans
  • Experience leading architecture reviews establishing engineering standards and influencing technical decisions across teams
  • Ability to balance strategic architecture with delivery constraints incremental modernization and pragmatic trade offs
  • Strong written and verbal communication skills for technical and non technical stakeholders
  • Hands on experience with Microsoft Azure and cloud native services
  • Experience with Docker and containerized application deployment
  • Experience with Kubernetes or Azure Kubernetes Service
  • Familiarity with infrastructure as code CI/CD automated deployment environment management and cloud cost optimization
  • Understanding of application security secrets management resilience disaster recovery and least privilege access
  • Experience designing APIs and integrations across distributed systems
  • Understanding of event driven architecture messaging asynchronous processing eventual consistency retries idempotency and dead letter handling
  • Experience with data pipelines data modeling and analytical platforms
  • Hands on experience with Snowflake
  • Familiarity with Azure Event Hubs Service Bus Kafka or equivalent messaging platforms
  • Understanding of data governance data quality access controls and privacy considerations
  • Practical experience adopting AI assisted development tools and integrating AI into software engineering workflows
  • Understanding of the software development lifecycle and AI support across requirements design coding testing review deployment and operations
  • Familiarity with Datadog
  • Experience with observability practices including metrics logs distributed tracing dashboards and alerts
  • Understanding of retrieval augmented generation patterns
  • Familiarity with model APIs and prompt engineering
  • Experience contributing to architecture reviews and technical design workshops
  • Experience supporting automated testing across unit integration contract end to end performance and security testing
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