Data Engineering Manager: Build Scalable Pipelines

GlobeTelecom; Philippines; On-site; PHP 1,200,000 - 2,000,000; Full time

Job summary

Globe is seeking a Data Engineering Manager to design, build, and maintain automated data pipelines across multi-platform environments. You will ensure data integrity, governance, and scalable data platforms aligned with the organization’s strategy.

You will lead cross-functional teams and drive end-to-end data flows using tools like Spark, Airflow, dbt, Snowflake, and Databricks, while ensuring secure and compliant processes.

Qualifications

• 3–7 years of progressive experience in ETL/ELT development, data pipeline design, and enterprise data engineering.
• Proven track record in managing and optimizing automated data pipeline systems within Big Data and cloud-native environments.
• Hands-on experience with distributed computing, data integration frameworks, and real-time streaming architectures.
• Experience in incident resolution, root cause analysis, and support for production-grade systems.
• Experience in telecom/fintech/enterprise tech sectors is a plus.

Responsibilities

• Design, build, and optimize automated data pipelines across multi-platform environments.
• Ensure data ingestion, transformation, and loading meet scalability, security, and business objectives.
• Lead data governance, data quality, and metadata management practices.
• Provide L3 support, troubleshoot complex pipeline and platform issues with QA/DevOps.
• Collaborate with Solution Architects, Data Architects, Product Owners, and Infrastructure teams.

Skills

Airflow

dbt

Kafka

Apache Spark

Talend

NiFi

SQL

PL/SQL

Python

Tools

Snowflake

Hadoop

AWS

GCP

Azure

S3

Glue

EMR

Job description

Globe is seeking a Data Engineering Manager to design, build, and maintain automated data pipelines across multi-platform environments. You will ensure data integrity, governance, and scalable data platforms aligned with the organization’s strategy.

You will lead cross-functional teams and drive end-to-end data flows using tools like Spark, Airflow, dbt, Snowflake, and Databricks, while ensuring secure and compliant processes.