Lead Data Engineer & DevOps

Location: Tan Binh, Ho Chi Minh City, Vietnam

Salary range: 70-75M VND gross

Job Summary:

We are looking for a Senior DevOps & Data Engineer to lead the design and implementation of scalable data pipelines and infrastructure, with a strong focus on Model Context Protocol (MCP). This role combines leadership, system architecture, and hands-on engineering to support data-driven applications and model lifecycle management.

Key Responsibilities:

DevOps Responsibilities:

Lead the development and maintenance of CI/CD pipelines for model deployment and data services.

Manage infrastructure automation using tools like Terraform, Ansible, or Kubernetes.

Ensure system reliability, scalability, and security across environments.

Monitor and optimize cloud/on-prem resources and performance.

Collaborate with engineering and data science teams to streamline deployment workflows.

Data Engineering Responsibilities:

Design and implement robust data pipelines that support MCP-based model context workflows.

Build and maintain ETL/ELT processes for structured and unstructured data.

Ensure data integrity, lineage, and traceability across model contexts.

Integrate data pipelines with model registries, versioning systems, and metadata stores.

Work closely with model developers to ensure seamless data-model interaction.

Leadership Responsibilities:

Lead and mentor a team of DevOps and Data Engineers.

Define and enforce best practices for infrastructure and data pipeline development.

Collaborate with cross-functional teams to align technical solutions with business and modeling goals.

Provide technical guidance and hands-on support when needed.

Requirements:

5 years of experience in DevOps OR/AND Data Engineering roles.

We are open to candidate with strong experience in one stack: DevOps or Data; and willing to learn the other.

Strong understanding of Model Context Protocol (MCP) and its application in model lifecycle management.

Experience with data pipeline tools (e.g., Apache Airflow, Spark, Kafka) and orchestration frameworks.

Proficiency in scripting languages (Python, Bash, etc.) and infrastructure tools (Docker, Kubernetes).

Familiarity with model registries, metadata management, and version control systems.

Bachelor's degree in Computer Science, Data Engineering, or related field.