Lead Data Engineer IRC252329

Description:

In this role, you will lead the development and implementation of data infrastructure and solutions using Microsoft Fabric. You will be responsible for architecting data pipelines, managing cloud data environments, and optimizing analytics workflows to support business intelligence and advanced data analytics initiatives.

Requirements:

Technical Expertise:

• Strong experience with Microsoft Fabric, Power BI, Azure Data Factory.
• Proficiency in SQL, Python, or other data programming languages.
• Experience with data warehousing, Data Lakehouse and big data
  technologies.
• Familiarity with medallion design pattern for Data Lakehouse solutions.

Leadership Skills:

• Proven ability to lead cross-functional teams and manage multiple
  projects.
• Strong communication skills to convey technical concepts to non-technical
  stakeholders.

Problem Solving:

• Analytical mindset with the ability to solve complex data challenges.
• A proactive approach to identifying and mitigating risks in data projects.

Experience:

• Bachelor’s degree in Computer Science, Data Engineering, or a related
  field.
• 5+ years of experience in data engineering, with a focus on cloud-based
  solutions.

Job Responsibilities:

Data Architecture and Strategy:

• Design and implement scalable data solutions leveraging Microsoft Fabric’s capabilities.
• Define best practices for data modeling, storage, and integration across the organization.

Pipeline Development:

• Develop and manage end-to-end data pipelines to ingest, transform, and load (ETL/ELT) data from multiple sources.
• Ensure the accuracy, integrity, and performance of data pipelines.

Analytics Enablement:

• Collaborate with business intelligence teams to design and deploy analytics solutions using Power BI and Microsoft Fabric.
• Provide actionable insights by integrating data with analytics tools and platforms.

Cloud Data Management:

• Manage cloud-based data storage solutions such as Azure Onelake.
• Monitor and optimize data infrastructure for cost efficiency and performance.

Leadership and Collaboration:

• Lead a team of data engineers and analysts to achieve project goals.
• Partner with stakeholders across application groups to understand data needs and deliver tailored solutions.