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.