GHN

Tóm tắt công việc

About GHN

Giao Hang Nhanh (GHN) is one of Vietnam's leading logistics enterprises, confidently providing superior services: "SMARTER, FASTER, MORE COST-EFFECTIVE." We support millions of businesses in optimizing their operations and create thousands of jobs for the Vietnamese people.

Established with the mission to revolutionize the logistics industry and driven by a technology-centric development approach, GHN continuously applies advanced technological solutions to every order. This aims to streamline operational processes, enhance delivery efficiency, and provide fast, secure, and effective service experiences for all customer segments – from e-commerce platforms and retailers to individual consumers.

With a nationwide network covering all 34 provinces and a strategy of continuous innovation alongside a robust infrastructure system, GHN has been affirming its pioneering position in the rapidly growing e-commerce and logistics sectors in Vietnam.

Why Join GHN?

This is a pivotal opportunity to join a rapidly growing e-commerce delivery leader in Vietnam, where your contributions will directly impact our strategic direction and market position. You will be part of a team that embraces cutting-edge technologies, data- driven innovation & discruption to solve real-world problems and enhance millions of lives. We offer a dynamic and collaborative environment where you can leverage your expertise, grow professionally through challenging projects, and make a significant impact on our success.

About the AI & Data Team

At GHN, our highly motivated AI & Data team is dedicated to establishing GHN as the fastest, most reliable, and convenient delivery service in Vietnam. We achieve this by developing and adopting advanced, data-driven decisions and solutions, making data capability a core business driver for operational efficiency, scalability, and customer trust and happiness.

Job Summary

We are seeking an exceptionally skilled and experienced Senior DataOps Engineer to manage, automate, and scale our high-performance, open-source-focused data platform. This role is central to our data strategy, requiring a blend of DevOps principles, deep infrastructure expertise, and data engineering fundamentals. The ideal candidate will ensure the reliability, security, and efficiency of our end-to-end data lifecycle, from ingestion and streaming to computation and visualization.

Key Responsibilities

• Platform Design & Automation: Architect, deploy, and maintain scalable, robust, and secure data infrastructure on Google Kubernetes Engine (GKE). Focus on automation using advanced scripting (Python, Go, Shell) and Kubernetes-native tools (e.g., manifests) across development, staging, and production environments.
• Orchestration & Data Flow: Design, implement, and optimize complex data pipelines using Apache Airflow. Ensure reliable data movement and scheduling for batch and streaming workloads.
• Streaming & Real-Time Data: Oversee and manage our diverse streaming ecosystem, including Kafka and RabbitMQ. Implement and administer Change Data Capture (CDC) processes using Kafka Connect with providers like Debezium, Confluent, and Lenseio.
• Data Governance & Security: Enforce data access control and audit policies using Apache Ranger across compute layers. Implement security best practices using Google Secret Manager and KMS for sensitive data and credentials.
• CI/CD & Deployment: Own and enhance the continuous integration and deployment (CI/CD) pipeline for data services using GitLab and ArgoCD, ensuring fast, reliable, and consistent environment rollouts.
• Performance & Optimization: Tune and optimize compute engines (Trino, Spark, Doris) and query performance on BigQuery. Manage storage layer efficiencies on GCS.
• Monitoring & Observability: Establish and maintain comprehensive monitoring, logging, and alerting using tools like Grafana to ensure high availability and proactive issue resolution.
• Metadata & Cataloging: Maintain and leverage metadata management systems (Hive Metastore, DataHub) to ensure data discoverability and quality across the organization.

• 13th-month salary, performance bonuses, and KPI bonuses
• Health insurance, Social insurance, and Unemployment insurance
• Medical insurance
• Annual health checkup
• Annual salary reviews
• Annual team building/company trip
• Ongoing internal and external training opportunities.

Our Comprehensive Data Platform Stack

Candidates should have demonstrable, deep experience across the majority of the following technologies:

• Compute & Query: Trino (or Presto), Apache Spark, Google BigQuery, Apache Doris
• Streaming & Messaging: Apache Flink, Apache Kafka, RabbitMQ
• CDC & Connectors: Kafka Connect (e.g., Debezium, Confluent)
• Infrastructure & Orchestration: Google Kubernetes Engine (GKE), Apache Airflow
• Databases: MongoDB, AlloyDB, MySQL, Redis
• Policy & Security: Apache Ranger, Google Secret Manager, KMS
• CI/CD & Tooling: GitLab, ArgoCD
• Languages: Python (Mandatory for DataOps), Java, Scala, Go
• Storage & Metadata: GCS, Hive Metastore, DataHub
• Table Format: Apache Iceberg
• Observability: Grafana, Superset, Metabase, Looker

Qualifications

• Experience: 3+ years in a DevOps or DataOps role focused on building and managing large-scale, distributed data platforms.
• Cloud Native: Deep expertise in cloud-native architecture, particularly on Google Cloud Platform (GCP).
• Automation & Scripting: Proficiency in Python and Shell scripting is mandatory. Experience with Go or Java/Scala for supporting data services is a strong asset.
• Containers & Orchestration: Advanced hands-on experience with Docker and Kubernetes (GKE), including writing custom operators or complex deployment manifests.
• Operational Excellence: Proven track record of designing, implementing, and maintaining high-SLA systems, with a strong focus on security, observability, and disaster recovery.
• Communication: Excellent communication skills, capable of explaining complex technical issues to both technical and non-technical stakeholders.

Education:

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative technical field.
• Master's degree in a related technical field is highly preferred.