Devops Lead

Role Summary We are looking for a Full Stack / DevOps Lead to build and operate the application layer and delivery platform of our Data Quality Factory. The role covers the user-facing applications, APIs and integrations that let business users define rules, review data quality results and approve remediation, along with the CI/CD pipelines, cloud infrastructure and operational practices that keep the platform reliable, secure and scalable. The Lead works closely with data engineers, AI engineers and business stakeholders, and manages a small engineering team.

Key Responsibilities

• Application development: Design and build full stack applications for the Data Quality Factory, such as rule management, data quality dashboards, remediation review and approval workflows, and exception handling.
• APIs and integrations: Build and maintain backend services and REST APIs; integrate with SAP and other enterprise systems, cloud data platforms (for example Databricks or Snowflake) and AI services.
• CI/CD: Set up and own build, test and deployment pipelines with automated testing, code quality checks and release management across environments.
• Infrastructure as code: Provision and manage cloud infrastructure using tools such as Terraform or equivalent; manage environments, networking, secrets and access.
• Containers and orchestration: Containerize services and run them on orchestration platforms such as Kubernetes or equivalent managed services.
• Monitoring and reliability: Implement logging, monitoring, alerting and observability; define SLAs, manage incidents and drive root cause analysis and continuous improvement.
• Security and compliance: Apply security best practices, including authentication and authorization, role-based access, secrets management, vulnerability scanning and audit logging; support data privacy and compliance requirements.
• Architecture and standards: Define engineering standards, reusable components, code review practices and documentation; make sound build-versus-buy decisions.
• Team and stakeholder management: Lead and mentor developers and DevOps engineers, plan sprints and delivery, and communicate status, risks and priorities to business and IT stakeholders.
• Support AI and data teams: Enable deployment of Python-based data quality jobs and AI or LLM-based remediation services into production in a repeatable, secure way.

Required Qualifications

• and Experience Bachelors or Master’s degree in Computer Science, Engineering or a related field. [X]+ years of software engineering experience, including [X–Y] years in full stack development and DevOps roles, with experience leading teams.
• Strong frontend experience with a modern framework such as React, Angular or Vue, along with HTML, CSS and TypeScript or JavaScript.
• Strong backend experience in Python, Java, Node.js or similar, with REST API design and SQL databases.
• Hands-on CI/CD experience (for example Azure DevOps, GitHub Actions, GitLab CI or Jenkins).
• Experience with a major cloud platform (Azure, AWS or GCP) and infrastructure as code.
• Experience with Docker and Kubernetes or equivalent container platforms.
• Experience with monitoring and logging tools and with production support practices.
• Good understanding of application security and enterprise access management.
• Strong communication, stakeholder management and people leadership skills.

Preferred Qualifications

• Experience working with data platforms such as Databricks or Snowflake and with data pipelines.
• Familiarity with SAP data and integrations, and with data quality or data governance concepts.
• Experience deploying AI or LLM-based services, including model APIs and MLOps / LLMOps practices.
• Experience with workflow engines and orchestration tools.
• Relevant cloud or DevOps certifications.
• Experience in Agile or hybrid delivery models.

Key Skills

• and Competencies React / Angular, TypeScript, Python / Java / Node.js, REST APIs CI/CD, Terraform, Docker, Kubernetes Azure / AWS / GCP, monitoring and observability Application and cloud security Team leadership, stakeholder management and communication Key Deliverables / Success Measures Production-ready Data Quality Factory applications and APIs delivered on schedule.
• Automated CI/CD pipelines and repeatable environments.
• Platform availability, performance and security targets met.
• Documented standards and a well-supported engineering team.