Data Platform Engineer

V2 Solutions; Hinoba-an; On-site; PHP 300,000 - 540,000; Full time

Job summary

V2 Solutions is seeking a Junior Data Platform Engineer to support Apache Spark, Airflow, and JupyterHub environments in a Linux-based data platform.

You will collaborate with senior engineers to deploy, monitor, and optimize data processing pipelines, while gaining hands-on experience in big data engineering and DevOps practices.

Qualifications

• 1+ years of experience in data platform or big data environments.
• Strong foundation in Python and Linux, with scripting experience.
• Familiarity with Apache Spark, Hive, Hadoop, and Airflow is required.
• Basic understanding of containers and version control.

Responsibilities

• Assist in setting up, monitoring, and maintaining Spark clusters and Airflow workflows.
• Support data pipelines, ETL jobs, and Python automation scripts.
• Help manage JupyterHub for multi-user access and integration with Spark.
• Monitor cluster health and troubleshoot job failures under guidance.
• Participate in code reviews, documentation, and deployment activities.

Skills

Apache Spark

Apache Hive

Hadoop File System

Python scripting

Linux

Git

Airflow

JupyterHub

CI/CD

DevOps basics

Education

Bachelor's or relevant degree

Tools

Docker

Kubernetes

Hadoop ecosystem tools

SQL

Job description

Job Scope

We are looking for an enthusiastic Junior Data Platform Engineer to support and manage our Apache Spark, Apache Airflow, and JupyterHub environments. This role is ideal for someone with a strong foundation in Python and Linux, who is eager to build a career in big data engineering and data platform administration. You will work closely with senior engineers to ensure smooth operation, deployment, and optimization of our data processing ecosystem.

Total /Relevant Experience

1+ years experience

Key Responsibilities

• Assist in the setup, monitoring, and maintenance of Apache Spark clusters, Apache Hive, Hadoop and Airflow environments.
• Support the development and scheduling of data pipelines using Airflow DAGs and Python scripts.
• Help manage and configure JupyterHub for multi-user access and integration with Spark.
• Monitor cluster health and performance under guidance and assist in troubleshooting Spark job failures.
• Write and maintain Python automation scripts for data workflows, ETL, and process automation.
• Participate in code reviews, documentation, and deployment activities.
• Learn and follow best practices for distributed data processing, CI/CD, and DevOps workflows.
• Collaborate with senior engineers and data scientists to implement improvements and new features.

Must-have skill

• Basic understanding of Apache Spark, Apache Hive and Hadoop File System.
• Familiarity with Apache Airflow (understanding of DAGs, scheduling, and task dependencies).
• Hands-on experience with Python scripting (data processing, automation, or API interaction).
• Comfortable working in Linux and container environments (command line, system logs, process management).
• Good understanding of data processing concepts, including ETL and distributed computing.
• Basic knowledge of Git and version control.

Good-to-Have Skills

• Exposure to Jupyter / JupyterHub for collaborative notebook environments.
• Knowledge of Docker or Kubernetes.
• Knowledge of Hadoop and Apache Spark Cluster.
• Familiarity with SQL and working with structured/unstructured data.
• Experience with cloud platforms (AWS, GCP, or Azure) is a plus.
• Interest in big data (Hadoop), DevOps, and data pipeline automation.

Qualifications Criteria

• Bachelor's or any relevant Degree.

Certification Criteria

• NA

Disclaimer

This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.