Lead Data Engineer

We are seeking a Lead Data Engineer to spearhead the customer`s cloud data initiatives, combining strong leadership with hands-on technical expertise. The ideal candidate will drive the design, development, and deployment of robust data solutions in Microsoft Azure, leveraging tools like Databricks, Unity Catalog, Asset Bundles, and Azure Data Factory.

This is a key role responsible for both guiding engineering teams and delivering scalable, secure, and high-performance data infrastructure.

Requirements:

• Proven experience in a Lead Data Engineer or Senior Data Engineer role.
• Strong hands-on experience with Azure cloud services, especially in data architecture and data engineering.
• Expert-level knowledge in Databricks, including Unity Catalog, Asset Bundles, and legacy migration strategies.
• Proficiency with Azure Data Factory for building and orchestrating data workflows.
• Deep understanding of data modeling, ETL/ELT processes, and data governance.
• Excellent communication skills and experience leading or mentoring technical teams.
• Bachelor`s or Master`s degree in Computer Science, Engineering, or a related field.

Nice to have:

• Azure certifications (e.g., Azure Data Engineer Associate).
• Experience with CI/CD pipelines and Infrastructure as Code (IaC).
• Familiarity with Delta Lake, Spark optimization techniques, and real-time data
• processing.
• English: Upper intermediate

Responsibilities:

• Lead the migration of legacy Databricks environments to Unity Catalog, including planning, execution, and change management.
• Lead and mentor a team of data engineers, providing technical direction and support.
• Architect, develop, and manage scalable data pipelines and integrations on the Azure cloud platform.
• Design and implement solutions using Azure Data Factory, Databricks, and Unity Catalog.
• Enforce data governance, security, and compliance standards within cloud data environments.
• Collaborate cross-functionally with data scientists, analysts, and business stakeholders to understand data needs.
• Manage and deploy Asset Bundles to promote reusability and governance in data workflows.
• Monitor, optimize, and troubleshoot large-scale ETL/ELT pipelines for performance and reliability.