Fabric Data Engineer
Builds, maintains and operates data pipelines, Lakehouse/Warehouse tables and semantic model integrations on the organisation's Microsoft Fabric platform, following designs, governance and CI/CD standards set by the Fabric Data Lead. Core stack: Microsoft Fabric, SQL, Python/PySpark, Azure DevOps and Power BI.
- Compensation
- Not disclosed
- Location
- Mandaluyong City, Metro Manila
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3-7 years' experience in data engineering, or a combination of data engineering and software engineering
- Minimum 1 year of hands-on Microsoft Fabric project experience
- Tertiary qualification in computer science, IT, engineering or related discipline
- Experience building ingestion and transformation pipelines across medallion (bronze/silver/gold) architecture
- Experience developing and maintaining Lakehouse and Warehouse tables
- Experience working within a defined CI/CD and code-review process
- Ability to build and maintain Fabric pipelines and notebooks
- Solid SQL proficiency
- Python and PySpark / Spark SQL for notebook development
- Azure DevOps and Git-based version control
- Power BI dataset integration and basic DAX
- Testing frameworks for data pipelines and notebooks
Nice to have
- Experience supporting Power BI Direct Lake implementations
- Experience monitoring Fabric capacity and remediating CU spikes
- Experience delivering in an offshore or distributed delivery model
- Experience contributing to root-cause analysis and production hotfix support
- Prior commercial software development experience
- Relevant Microsoft certification in Fabric or Azure data engineering
- Data lifecycle and retention, row-level security and sensitivity labelling
- Performance tuning of Spark notebooks and pipelines
Full description
Schedule: Monday to Friday 6am to 3pm PHTFull OnsitePurpose of the Role
The Fabric Data Engineer builds, maintains and operates the pipelines, Lakehouse objects and semantic model integrations that make up the organisation's Microsoft Fabric platform, working to designs and standards set by the Fabric Data Lead.
This is an execution-focused role. Success is measured by reliable, well-tested delivery against agreed tickets, consistent adherence to governance and CI/CD standards, and clear, early communication of blockers or quality risks back to the Lead.
Who you are?Required Technical Skills
- At least 3 to 7 years’ experience in a data engineering role, or a combination of data engineering and software engineering.
- Minimum 1 year of hands-on Microsoft Fabric project experience.
- Tertiary qualification in computer science, information technology, engineering or a related discipline
- Demonstrated experience building and maintaining ingestion and transformation pipelines across a medallion (bronze/silver/gold) architecture.
- Experience developing and maintaining Lakehouse and Warehouse tables.
- Experience working within a defined CI/CD and code-review process.
- Fabric pipelines and notebooks — build and maintain.
- Solid SQL proficiency.
- Python and PySpark / Spark SQL for notebook development.
- Lakehouse and Warehouse table development.
- Working knowledge of medallion (bronze/silver/gold) architecture and its practical implementation.
- Azure DevOps and Git-based version control.
- Power BI dataset integration and basic DAX.
- Testing frameworks for data pipelines and notebooks.
Nice to Have Technical Skills
- Experience supporting Power BI Direct Lake implementations.
- Experience monitoring Fabric capacity and remediating CU spikes.
- Experience delivering in an offshore or distributed delivery model.
- Experience contributing to root-cause analysis and production hotfix support.
- Prior experience of developing commercial software development
- Relevant Microsoft certification in Fabric or Azure data engineering.
- Direct Lake mode implementation and troubleshooting.
- Fabric capacity monitoring and CU remediation.
- Data lifecycle and retention, row-level security and sensitivity labelling.