Data Engineer

We are a company that helps organizations transform business performance through advanced analytics, cloud technologies, AI, and digital engineering. We partner with global enterprises to build scalable data platforms that support business intelligence, automation, and AI-driven decision making. Our focus is on delivering enterprise-grade solutions that turn complex data into reliable, high-value outcomes for modern businesses. The team works on large-scale technology programs where data architecture, platform engineering, and analytics capabilities must come together to improve speed, quality, and decision-making.

This role sits within an environment that values strong technical execution, collaboration, and continuous improvement across data delivery. The organization is looking for a Fabric Data Engineer who can help build modern Lakehouse-based platforms using Microsoft Fabric. The work will directly support reporting, advanced analytics, and AI/ML use cases.

• Partners with global enterprises to build scalable data platforms that power business intelligence, automation, and AI-driven decision making.
  
  - Works across advanced analytics, cloud technologies, AI, and digital engineering to support business transformation.
  
  - Delivers enterprise-scale data solutions that serve reporting, analytics, and AI/ML initiatives.

• Collaborates closely with business stakeholders, data scientists, and engineering teams to deliver high-quality solutions.
  
  - Operates in a collaborative global delivery environment with strong emphasis on continuous learning and career growth.
  
  - Values troubleshooting, optimization, and reliability in data platform delivery.

This role owns the design, build, and optimization of enterprise-scale data platforms using Microsoft Fabric. The focus is on creating Lakehouse architectures, scalable ETL/ELT pipelines, and high-performance data solutions that enable reporting, advanced analytics, and AI/ML initiatives. The engineer will work across ingestion, transformation, orchestration, and integration layers to keep data assets reliable, governable, and ready for consumption. Success in this role means delivering stable platforms that improve data availability, support modern analytics workflows, and accelerate downstream decision-making.

The position also requires close collaboration with stakeholders and technical teams to ensure data solutions stay aligned with business needs and performance expectations.

• Design, develop, and maintain scalable data platforms using Microsoft Fabric so enterprise data can be delivered reliably at scale for reporting and analytics.
  
  - Build and optimize ETL/ELT pipelines with Data Factory, Spark notebooks, and PySpark to improve data movement, transformation speed, and repeatability.
  
  - Develop Lakehouse solutions using One Lake and Delta Tables to create a structured foundation for governed, high-performance data access.
  
  - Implement robust ingestion, transformation, and orchestration workflows that keep data pipelines dependable, observable, and ready for production use.
  
  - Support AI/ML initiatives by preparing curated datasets and feature‑engineered inputs that improve model readiness and analytical value.
  
  - Integrate Microsoft Fabric with Power BI, semantic models, and enterprise analytics platforms so business teams can consume trusted data efficiently.
  
  - Ensure governance, quality, security, and compliance across data assets to reduce risk and protect the integrity of enterprise data.
  
  - Monitor, troubleshoot, and optimize pipeline performance, scalability, and reliability to keep data services stable under changing demand.
• Microsoft Fabric expertise with hands‑on experience in building enterprise data platforms, Lakehouse patterns, and operational analytics workflows.
  
  - Strong skills in Data Factory, Spark notebooks, and PySpark to design and support scalable ETL/ELT processing across large datasets.
  
  - Advanced SQL and distributed data processing knowledge to transform, model, and query data efficiently across modern cloud environments.
  
  - Practical experience with One Lake, Delta Tables, and data modeling best practices to build structured and reusable data foundations.
  
  - Working knowledge of data governance, security, quality management, and performance tuning to keep platforms reliable and compliant.
  
  - Experience integrating Power BI, semantic models, and enterprise reporting platforms so data products serve business users effectively.
  
  - Familiarity with Azure Data Services, CI/CD, Git, and Microsoft Purview to support disciplined engineering and controlled delivery.

• Experience supporting enterprise AI and analytics platforms, especially where data preparation directly influences downstream model or insight quality.
  
  - Exposure to Dev Ops practices for data engineering, including automated delivery, version control, and release discipline.
  
  - Background in large-scale cloud…