Data Engineer

UL Solutions External Career Site; Makati; On-site; PHP 600,000 - 900,000; Full time

Job summary

UL Solutions External Career Site is seeking a data engineer to profile, analyze, and validate source data, and to design scalable pipelines. You will work with internal and external data sources to understand structure, quality, and transformation requirements.

You will develop and optimize SQL-based components, implement data quality checks, automate testing and CI/CD, monitor pipelines, and collaborate with data owners, security teams, and platform engineers to ensure reliable data delivery.

Responsibilities

• Profile, analyze, and validate source data to understand structure, quality.
• Design, develop, test, and maintain data pipelines that ingest and transform data from internal and external sources.
• Build scalable ETL/ELT solutions across cloud data platforms and data warehouses.
• Develop and optimize SQL, stored procedures, scripts, and reusable data engineering components.
• Implement data quality checks, reconciliation controls, error handling, logging, lineage capture, and pipeline monitoring.
• Apply automated testing, version control, code review, continuous integration, and deployment practices.
• Monitor pipeline execution, investigate failures, resolve data issues, and support root cause analysis.
• Collaborate with data owners, data stewards, architects, security teams, and platform engineers.
• Document technical designs, mappings, transformation logic, procedures, dependencies, and support information.
• Read and follow the Underwriters Laboratories Code of Conduct and all security practices.

Job description

• Profile, analyze, and validate source data to understand structure, quality, business meaning, and transformation requirements.
• Design, develop, test, and maintain data pipelines that ingest, transform, and deliver data from internal and external sources.
• Build scalable ETL/ELT solutions across cloud data platforms, data warehouses, lakehouse patterns, and data integration tools.
• Develop and optimize SQL, stored procedures, scripts, and reusable data engineering components.
• Implement data quality checks, reconciliation controls, error handling, logging, lineage capture, and pipeline monitoring.
• Apply automated testing, version control, code review, continuous integration, and deployment practices.
• Monitor pipeline execution, investigate failures, resolve data issues, and support root cause analysis.
• Collaborate with business users, data owners, data stewards, architects, security teams, and platform engineers.
• Document technical designs, mappings, transformation logic, operational procedures, dependencies, and support information.
• Read and follow the Underwriters Laboratories Code of Conduct and all physical and digital security practices.