Data Engineer: Cognos & Power BI Dashboards

ING Hubs Philippines; Metro Manila; On-site; PHP 900,000 - 1,200,000; Full time

Job summary

ING Hubs Philippines is seeking a Data Engineer to join the Retail Banking team. You will develop analytics and reporting solutions from data sourcing to dashboarding, collaborating with business stakeholders and technical teams to deliver data-driven insights that drive measurable value.

You will design data handling processes, implement data ingestion, transformation, and storage, and build dashboards using Cognos and Power BI while ensuring data quality and reliability across reporting assets.

Qualifications

• 2-5 years of experience in a data-related role
• Experience with ETL and data integration tools
• Experience with data warehousing concepts and BI reporting

Responsibilities

• Develop and maintain analytics and reporting solutions from data sources to dashboards
• Gather requirements and translate business needs into scalable data and reporting solutions
• Extract, transform, validate, and integrate data from multiple sources
• Design data handling processes including ingestion, transformation, modelling, and storage
• Build dashboards and reports using Cognos and Power BI
• Present insights and recommendations to stakeholders
• Ensure data quality, consistency, and reliability across reporting assets

Skills

SQL

RDBMS

Data modelling

Data transformation

Data ingestion

Stakeholder communication

Education

Bachelor's degree in Computer Science or related

Tools

IBM DataStage

Cognos

Power BI

Python

Job description

ING Hubs Philippines is seeking a Data Engineer to join the Retail Banking team. You will develop analytics and reporting solutions from data sourcing to dashboarding, collaborating with business stakeholders and technical teams to deliver data-driven insights that drive measurable value.

You will design data handling processes, implement data ingestion, transformation, and storage, and build dashboards using Cognos and Power BI while ensuring data quality and reliability across reporting assets.