Role Profile Senior hands-on engineer with expertise in production cloud and data environments, complex incident resolution, infrastructure automation, DataOps, and agentic AI. Capable of diagnosing and remediating performance, reliability, security, and technical-debt issues while developing repeatable engineering practices and scalable operational tooling. Core Technical Responsibilities
• Engineer, operate, troubleshoot, and optimize production cloud and data platforms.
• Diagnose critical outages, multi-system failures, pipeline disruptions, and performance bottlenecks; conduct root-cause analysis and implement durable fixes.
• Audit and reduce technical debt, security vulnerabilities, and operational risks across cloud infrastructure and data systems.
• Build and optimize DataOps practices, including CI/CD for data pipelines, data-quality monitoring, workload orchestration, and data lifecycle automation.
• Design and maintain reusable Infrastructure-as-Code (IaC) modules, automated runbooks, and standardized DevOps/DataOps frameworks.
• Implement agentic AI workflows, LLM-powered operational assistants, intelligent automation, and automated scripting or code conversion to improve operational efficiency.
• Introduce proactive observability, automated compliance checks, and self-healing infrastructure patterns.
• Produce technical documentation, architecture guidance, and operational playbooks; support knowledge transfer and engineering mentorship. Required Technical Competencies
• 7+ years of hands-on experience in cloud architecture, DevOps engineering, and complex production environments.
• Cloud platforms: practical expertise in one or more of AWS, Microsoft Azure, and Google Cloud Platform (GCP).
• Data engineering / DataOps: experience implementing and maintaining data pipelines,



data-quality controls, orchestration, and automated data workflows; strong experience with Azure Data Factory and Databricks highly preferred.
• Infrastructure as Code and configuration management: Terraform, AWS CloudFormation, Azure Bicep, and/or Ansible.
• Containers and orchestration: Kubernetes and/or Amazon ECS.
• CI/CD: design and maintenance of automated deployment pipelines for applications, infrastructure, and data workflows.
• Observability and monitoring: Datadog, Amazon CloudWatch, Dynatrace, or comparable tools.
• Incident engineering: advanced troubleshooting, systematic root-cause analysis, remediation, and resilience improvements.
• AI automation: hands-on implementation of agentic AI frameworks, LLM tools, automated runbooks, and AI-assisted operational workflows.
• Security and reliability: identification and remediation of vulnerabilities, preventive controls, automated compliance, and performance optimization. Technical Working Capabilities
• Ability to operate independently in complex, high-pressure production incidents.
• Ability to design maintainable, modular, repeatable solutions across cloud and data environments.
• Ability to document technical decisions clearly, standardize processes, and mentor other engineers. Education and Preferred Certifications
• Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or a related quantitative discipline (preferred).
• SnowPro Core or SnowPro Advanced: Data Engineer (preferred).
• Databricks Certified Data Engineer Professional or Databricks Certified Associate Developer for Apache Spark (preferred).
• AWS Certified Data Engineer – Associate or AWS Certified Data Analytics – Specialty (preferred; certification names as stated in the source profile).

📌 Senior Cloud (Ciudad de México)
🏢 FactIQ
📍 Ciudad de México