Data Engineer with Microsoft Fabric

Job Title: Data Engineer Microsoft Fabric

Data Enginee

across cloud and on-prem ecosystems. Proven track record of developing high-performance ETL

pipelines, optimizing data infrastructure, and enabling analytics teams through reliable, well-

structured, and secure data systems. Deep expertise in Microsoft Fabric and GCP, with hands-on

experience across data ingestion, transformation, modeling, and delivery. Comfortable working in

fast-moving environments that require ownership, adaptability, and strong collaboration with

analytics and product teams.

Core Skills

Microsoft Fabric

• Designed and implemented pipelines and dataflows (Gen2) to orchestrate data ingestion

from multiple structured and semi-structured sources.

• Developed notebooks using Python and Spark SQL for data wrangling, feature engineering,

and model-ready transformations.

• Implemented CI/CD in Fabric using Azure DevOps for automated deployment of pipelines,

datasets, and reports.

• Defined security and access control strategies in Fabric, ensuring compliance with

enterprise data governance standards.

• Built and optimized Direct Lake models for low-latency Power BI reporting.
• Developed advanced DAX measures and tabular models to support complex analytical

dashboards.

Google Cloud Platform (GCP)

• Designed and optimized data warehouses using BigQuery, improving performance and

reducing query costs through partitioning and clustering.

• Automated workflows using Cloud Functions to handle event-based data transformations

and notifications.

• Integrated real-time streaming data through Pub/Sub, enabling near real-time analytics

pipelines.

• Languages:

  Python, SQL, Spark SQL
• Tools: Microsoft Fabric, Power BI, Azure DevOps, Git, GCP Console, Dataflow, Dataproc
• Concepts: Data Modeling (Kimball, Data Vault), ETL/ELT, CI/CD, Data Governance, Security,

Performance Optimization

Experience Highlights

• Led migration of on-prem ETL workflows to Microsoft Fabric, reducing maintenance effort

by 40% and improving pipeline reliability.

• Built an end-to-end analytics solution combining BigQuery with Power BI, enabling self-

service reporting for 100+ users.

• Designed a CI/CD pipeline in Fabric, automating dataset promotion from dev to production

with integrated testing and validation.

• Partnered with data scientists to deliver feature stores and ML-ready datasets using Fabric

Notebooks and Spark SQL.

• Implemented Fabric security model to support row-level access and data masking across

multiple business domains.

• Created a unified metadata catalog and data quality monitoring framework, improving data

discoverability and trust.