Data Engineering Lead - Cloud Platforms & Analytics

Palawan Group of Companies

Mandaluyong

On-site

PHP 900,000 - 1,800,000

Full time

2 days ago
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Job summary

Palawan Group of Companies in the Metro Manila area is seeking a Data Engineer Supervisor to lead design, development, and optimization of enterprise data platforms and pipelines. You will deliver scalable data solutions and guide a team of engineers to meet growing analytics needs.

You will drive data engineering best practices, governance, and collaboration with data analysts and scientists, while mentoring staff, reviewing code, and ensuring timely delivery of cloud-based data workloads using

Qualifications

  • Bachelor's degree in CS/IT/data engineering or related field.
  • At least 5 years in data engineering, data warehousing, or data platform development.
  • Experience leading, mentoring, or managing technical teams.
  • Strong grasp of data governance, security, and quality management.

Responsibilities

  • Design and implement scalable data pipelines for ingestion, transformation, and storage.
  • Build and maintain data warehouses and data lakes.
  • Develop and optimize ETL/ELT processes for efficiency and reliability.
  • Lead, mentor, and develop a team of Data Engineers.
  • Conduct code reviews and enforce standards and best practices.
  • Manage project timelines, priorities, and deliverables.
  • Collaborate with Data Analysts, Data Scientists, and stakeholders to deliver data-driven solutions.

Skills

Leadership
Communication
Analytical
Project planning

Education

Bachelor's Degree in Computer Science, Information Technology, Data Engineering, or a related field

Tools

Apache Airflow
AWS Data Services

Job description

Palawan Group of Companies in the Metro Manila area is seeking a Data Engineer Supervisor to lead design, development, and optimization of enterprise data platforms and pipelines. You will deliver scalable data solutions and guide a team of engineers to meet growing analytics needs.

You will drive data engineering best practices, governance, and collaboration with data analysts and scientists, while mentoring staff, reviewing code, and ensuring timely delivery of cloud-based data workloads using

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