Data Engineer: Build & Optimize Reliable Data Pipelines

Our Clients; Mandaluyong; On-site; PHP 600,000 - 1,000,000; Full time

Job summary

Our Clients is seeking a motivated Data Engineer to build, maintain, and improve data pipelines and infrastructure for data-driven products and initiatives. You’ll work with data engineers, data scientists, and cross-functional teams to ensure data accuracy, accessibility, and reliability.

Responsibilities include developing pipelines, monitoring performance, and implementing validation and governance practices.

Qualifications

• Bachelor's degree in CS/IT/Engineering or equivalent practical experience.
• 2–3 years of data engineering or related experience.
• Proficiency in Python, SQL, data warehousing, and ETL/ELT concepts.

Responsibilities

• Develop, deploy, and maintain data pipelines that support business use cases and product features.
• Monitor pipeline performance and reliability, troubleshoot data quality issues, and help resolve operational incidents.
• Support data requests and pipeline incidents within agreed timelines and service-level expectations.
• Implement data validation, testing, and monitoring practices to maintain data accuracy and reliability.
• Maintain clear technical documentation and follow established development and deployment procedures.
• Apply data engineering best practices, including version control, CI/CD, data governance, and observability.
• Collaborate with senior engineers and cross-functional stakeholders on data initiatives and continuous improvement.
• Identify opportunities to enhance pipeline performance, reliability, and usability.

Skills

Python scripting

SQL

Data pipelines

CI/CD

Data governance

Education

Bachelor's degree or equivalent in CS/IT/Engineering

Tools

Airflow

Dagster

AWS

GCP

Azure

Job description

Our Clients is seeking a motivated Data Engineer to build, maintain, and improve data pipelines and infrastructure for data-driven products and initiatives. You’ll work with data engineers, data scientists, and cross-functional teams to ensure data accuracy, accessibility, and reliability.

Responsibilities include developing pipelines, monitoring performance, and implementing validation and governance practices.