Fabric Data Engineer

Schedule: Monday to Friday 6am to 3pm PHT

Full Onsite

Purpose 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.

• Performance tuning of Spark notebooks and pipelines.

What will you do?

• Work within the defined CI/CD and code-review process, giving and receiving review feedback constructively.

• Communicate progress, blockers and quality risks to the Fabric Data Lead early and clearly.

• Maintain and grow technical capability in Microsoft Fabric, SQL and Python/PySpark.

• Participate in team ceremonies, planning and estimation, and honour agreed commitments.

• Integrate datasets into Datawarehouse and semantic models to agreed specifications.

• Support and troubleshoot Direct Lake mode implementations.

• Make agreed DAX and model changes under the model approval gate.

• Fix defects and production hotfixes within agreed SLAs.

• Understand the downstream reporting impact of changes before deploying them.

• Build and maintain Fabric pipelines and notebooks for ingestion and transformation across the bronze, silver and gold layers, to designs provided by the Fabric Data Lead.

• Develop and maintain Lakehouse and Warehouse tables, ensuring data quality and following agreed naming and workspace conventions.

• Work through the Code → Review → DevOps pipeline → Deployment process for every change.

• Apply the agreed testing framework to data pipelines and notebooks.

• Monitor own workloads and flag CU spikes or performance issues; optimise notebook and pipeline performance under guidance from the Lead.

• Contribute to root-cause analysis for incidents attributable to own work.

• Keep the asset catalogue, ownership register and lineage documentation current for all assets built or modified.

• Document pipelines, notebooks and models so others can support and extend them.

• Share lessons learned from incidents and delivery with the wider engineering and data community.

• Support onboarding and knowledge transfer across the onshore and offshore team.

• Optimise notebooks and pipelines to reduce capacity consumption and run cost.

• Build reusable, pattern-based solutions rather than one-off implementations.

• Deliver agreed scope predictably so the platform roadmap stays on track.

• Identify opportunities to simplify or automate recurring manual effort.