About the role

Lead Data Engineer

Job Summary:

As a Lead Data Engineer, you will play a pivotal role in designing and delivering enterprise-scale data solutions that drive business success. Your expertise will ensure that our data platforms are not only robust but also aligned with the latest AI innovations, ultimately enhancing our data governance and analytics capabilities.

Key Responsibilities:

Lead end-to-end data engineering and analytics delivery, ensuring quality and efficiency in all project phases.
Architect scalable Azure-based data platforms that meet the organization’s strategic objectives.
Drive AI and LLM use cases leveraging Azure OpenAI and enterprise data to deliver innovative solutions.
Define data standards, governance protocols, and engineering best practices to optimize data handling.
Mentor and develop engineering teams while coordinating efforts with global stakeholders for seamless project execution.
Provide technical leadership for compliance and regulatory reporting solutions to ensure alignment with industry standards.

Requirements:

10+ years of work experience in Data Engineering and Analytics, demonstrating a robust understanding of data ecosystems.
Strong expertise in Azure Data Factory (ADF) with at least 3 years of hands-on experience.
Proven experience with PySpark for data processing and transformation for a minimum of 3 years.
Hands-on experience designing enterprise-scale data platforms and creating data governance frameworks.
Proficient in Data Modeling, ETL/ELT processes, Data Quality, and Data Observability practices.
Strong stakeholder management skills and experience in solution architecture within cross-functional teams.

Preferred Qualifications:

Experience with Azure Databricks and its applications in data engineering projects.
Familiarity with Azure OpenAI, LLMs, RAG architectures, and Prompt Engineering.
Ability to thrive in a hybrid work environment and collaborate effectively with geographically dispersed teams.
Strong problem-solving skills and a commitment to continuous learning in the data domain.

Benefits:

Competitive salary with performance-based bonuses and incentives.
Flexible hybrid work environment to promote work-life balance.
Comprehensive health and wellness benefits, including medical, dental, and vision coverage.
Opportunities for professional development and continuous learning through training programs.
Collaborative and innovative work culture that encourages creativity and initiative.
Access to cutting-edge technology and tools to drive data innovation.