1/ Azure Data Lake Architecture :
Architect, design, and implement scalable and efficient data solutions utilizing Azure Data Lake Storage and related services.
2/ ETL Pipeline Development:
Lead the development of end-to-end ETL pipelines, ensuring seamless data movement, transformation and loading within the Azure Data Lake environment.
3/ Data Modelling and Integration:
Design and implement effective data models and integration strategies to support business requirements and analytical needs.
4/ Azure Services Integration:
Integrate Azure Data Lake with other Azure services, such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, to create comprehensive data processing workflows.
5/ Performance Optimization:
Optimize data storage, retrieval, and processing for maximum performance and efficiency within the Azure Data Lake ecosystem.
6/ Data Security and Governance:
Implement robust data security measures, including role-based access control (RBAC), encryption, and compliance with data governance policies.
7/ Collaboration:
Collaborate with cross-functional teams, data analysts, and business stakeholders to understand requirements and deliver effective data solutions.
8/ Monitoring and Maintenance:
Establish monitoring mechanisms and perform routine maintenance tasks to ensure the health and reliability of the Azure Data Lake environment.
9/ Documentation:
Create and maintain comprehensive documentation for data processes, architecture, and solutions within the Azure environment.
10/ Mentorship:
Provide mentorship and guidance to junior members of the data engineering team, fostering a culture of continuous learning.
Requirements:
A/ Azure Expertise:
Proven experience in designing and implementing data solutions using Azure Data Lake Storage and related Azure services.
B/ Technical Skills:
Proficient in data modeling, ETL processes, and programming languages (e.g., Python, C#) within the Azure ecosystem.
C/ Big Data Technologies:
Experience with Big Data technologies such as Apache Spark and Hadoop within the Azure environment.
D/ Problem Solving:
Strong analytical and problem-solving skills, with the ability to address complex data engineering challenges in the Azure Data Lake context.
E/ Communication Skills:
Excellent communication and collaboration skills, with the ability to convey technical concepts to non-technical stakeholders.
F/ Leadership:
Proven leadership experience in guiding and mentoring a team of data engineers in an Azure-centric environment.