[Hiring] Principal Data Engineer @Nexaminds

Role Description

Nexaminds is looking for a Principal Data Engineer to design, build, and evolve scalable data solutions within a Microsoft Azure and Databricks ecosystem. The ideal candidate brings deep hands-on experience with Databricks, Azure, and enterprise data modeling, combined with the ability to make architectural decisions, establish engineering best practices, and guide other engineers in the development of scalable and reliable data platforms.

Qualifications

• 10+ years of experience in Data Engineering, Data Architecture, or a related technical discipline.
• Familiarity with Microsoft technologies such as Azure Data Factory, Azure Synapse Analytics, Azure SQL, Microsoft Fabric, or Power BI.
• Experience with Python, SQL, or PySpark.
• Extensive hands-on experience with Databricks in enterprise data environments.
• Strong expertise in data modeling, including conceptual, logical, and physical data models.
• Extensive experience designing and implementing data solutions on Microsoft Azure.
• Strong understanding of the Microsoft data and technology ecosystem.
• Proven experience designing scalable, reliable, and maintainable data platforms and pipelines.
• Strong understanding of data architecture, data integration, data transformation, and data lifecycle concepts.

Requirements

• Design and implement scalable data engineering solutions using Databricks and Azure.
• Define and maintain enterprise data modeling standards and best practices.
• Lead the design of data pipelines, data transformations, and data integration solutions.
• Evaluate technical requirements and recommend appropriate data architectures and implementation patterns.
• Establish engineering standards for scalability, performance, reliability, and maintainability.
• Partner with architects and engineering teams to align data solutions with broader enterprise architecture.
• Provide technical leadership and mentorship to Senior Data Engineers and other members of the data engineering organization.
• Identify opportunities to improve data platform performance, reliability, and operational efficiency.
• Troubleshoot complex data engineering and platform issues.
• Contribute to architectural and technical decision-making across data initiatives.
• Support modernization efforts involving legacy data platforms and migration to cloud-based data architectures.
• Ensure data solutions follow established security, quality, and governance standards.

Benefits

• Stock options 📈
• Remote work options 🏠
• Flexible working hours 🕜
• Benefits above the law