Fabric Data Engineer - Build AI-Ready Data Pipelines

CLaaS2SaaS; Manila; On-site; PHP 600,000 - 900,000; Full time

Job summary

CLaaS2SaaS in Manila is seeking a Microsoft Fabric Data Engineer to design, build, and manage enterprise data solutions for analytics and AI workloads. You'll work with Fabric Lakehouses, Warehouses, and Pipelines, transforming raw data into structured datasets, while ensuring data quality, security, and governance across teams.

Collaborate with AI engineers, product managers, and business teams to deliver scalable data platforms and cost-optimized data pipelines that support RAG and LLM

Qualifications

• 3–6 years in data engineering, analytics, or platform engineering.
• Bachelor’s or Master’s in Computer Science, Data Engineering, or related field.
• Microsoft certifications: DP-600, AZ-204, DP-100.

Responsibilities

• Design and maintain Microsoft Fabric data architecture.
• Build ETL pipelines using Fabric, Synapse, or Azure Data Factory.
• Transform raw data into structured datasets for analytics and AI projects.
• Optimize data models and pipelines for performance, reliability, and cost.
• Support AI engineers for RAG and LLM workloads.
• Monitor pipeline performance, ensure data quality and governance.
• Document workflows and share knowledge with the team.

Skills

Microsoft Fabric

SQL

Spark / PySpark

Azure Data Factory

Azure Synapse

Data modeling

Data governance & security

Education

Bachelor’s or Master’s in Computer Science, Data Engineering, or related field

Microsoft certifications: DP-600, AZ-204, DP-100

Tools

Azure Data Factory

Azure Synapse

Dataverse

Power BI

Job description

CLaaS2SaaS in Manila is seeking a Microsoft Fabric Data Engineer to design, build, and manage enterprise data solutions for analytics and AI workloads. You'll work with Fabric Lakehouses, Warehouses, and Pipelines, transforming raw data into structured datasets, while ensuring data quality, security, and governance across teams.

Collaborate with AI engineers, product managers, and business teams to deliver scalable data platforms and cost-optimized data pipelines that support RAG and LLM