Cloud Data Engineer: AI-Ready Data Platforms (Hybrid)

NNIT; Makati; Hybrid; PHP 900,000 - 1,300,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

NNIT is seeking a Data Engineer in the Philippines to design, build, and maintain enterprise data platforms enabling AI, analytics, and digital transformation. You will collaborate with architects, AI engineers, consultants, and stakeholders across Europe, Asia, and beyond to deliver scalable, AI-ready data solutions.

Responsibilities include building scalable data pipelines, data lakes, semantic models, and secure data architectures while aligning with governance and DevOps practices.

Qualifications

• Bachelor's degree in a relevant field.
• 4–7 years of Data Engineering or Software Engineering experience.
• Strong SQL and Python programming skills.
• Experience building enterprise data platforms and ETL/ELT pipelines.
• Experience working with cloud-based data platforms.
• Experience delivering solutions using Agile methodologies.

Responsibilities

• Build Data Platforms to transform raw data into trusted, AI-ready information assets.
• Design and develop enterprise data pipelines using ETL/ELT frameworks and build scalable data lakes/lakehouses.
• Develop semantic models and AI-ready datasets for analytics and AI workloads.
• Engineer for scale with reliable, secure data architectures and observability.

Skills

SQL

Python

ETL/ELT

Cloud platforms

Agile

Data modeling

Education

Bachelor's degree in Computer Science, Engineering, IT, Data Science, or related field

Tools

Azure Data Factory

Databricks

Snowflake

DBT

Power BI

Microsoft Purview

Job description

NNIT is seeking a Data Engineer in the Philippines to design, build, and maintain enterprise data platforms enabling AI, analytics, and digital transformation. You will collaborate with architects, AI engineers, consultants, and stakeholders across Europe, Asia, and beyond to deliver scalable, AI-ready data solutions.

Responsibilities include building scalable data pipelines, data lakes, semantic models, and secure data architectures while aligning with governance and DevOps practices.