Principal Data Engineer (Remote, Lahore, PKR Salary)

Requirements:

• Bachelors or master's degree in computer science, Engineering, Information Technology, or a related field.
• 5 to 7 years of experience in data engineering with a strong focus on cloud-based solutions.
• Expertise in Azure Synapse Analytics, including data integration, management, and security.
• Proficient with SQL, Python, PySpark, and other scripting languages commonly used in data engineering.
• Familiar with Azure IaC (Infrastructure as Code) and CI/CD automation using ARM, Bicep or Terraform
• Deep knowledge of ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) processes and experience with Azure Data Factory.
• Strong understanding of data modeling (Data Vault 2.0 experience or certification preferred), data warehousing, and data lakes.
• Proven consulting or advisory roles in the design and deployment of large-scale data solutions in Azure environments.
• Experience working directly with clients or stakeholders in a consulting capacity, translating business needs into technical specifications and actionable plans.
• Demonstrated ability to contribute to project leadership, coordinate across client functional groups, and manage client relationships.
• Microsoft Certified: Azure Data Engineer Associate preferrable
• Microsoft Certified: Azure Solutions Architect Expert preferrable
• Certified Data Vault 2.0 Practitioner (CDVP2) preferrable

Responsibilities:

• Design and implement highly scalable, high performance data warehousing solutions in Azure Synapse.
• Analyze and migrate existing data systems to Azure Cloud, optimizing data flow and collection to improve data accuracy and utility.
• Contribute to data architecture design and modeling initiatives, ensuring alignment with business needs and compliance with data governance standards.
• Consult with stakeholders to define data requirements, deliverables, and timelines, providing technical leadership and guidance throughout project lifecycles.
• Implement data security and privacy policies in line with industry best practices and organizational standards.
• Optimize data integration pipelines for performance and scalability.
• Develop and maintain documentation regarding data architectures, procedures, and solutions.