Azure Data Engineer

• Data Pipeline Development:

Design, develop, and maintain data pipelines using Azure Data Factory for efficient data movement and transformation. Azure Synapse Analytics Implementation: Implement and optimize large-scale data warehousing solutions using Azure Synapse Analytics. Data Transformation and Processing: Utilize Azure Databricks for data transformation and processing, ensuring the scalability and efficiency of ETL workflows. Database Design and Management: Design and manage databases on Azure, optimizing for performance, scalability, and data integrity. Integration with Power BI: Integrate data from various sources into Power BI for creating insightful reports and dashboards.

• Big Data Processing:

Leverage big data technologies, including Apache Spark, for distributed and scalable data processing. Query Optimization: Optimize SQL queries to enhance database performance and ensure efficient data retrieval. Data Security and Compliance: Implement data security measures and ensure compliance with regulatory standards in Azure data solutions.

• Data Quality Management:

Develop and implement data quality checks and validation processes to maintain high-quality data. Scripting and Automation: Use scripting (Python, PowerShell) for automation of data-related tasks, enhancing efficiency and repeatability. Collaboration and Documentation: Collaborate with cross-functional teams, providing technical expertise and insights. Document data engineering processes, solutions, and best practices. Continuous Learning: Stay updated on the latest Azure data services, tools, and best practices. Continuously improve data engineering processes and solutions.

• Azure Data Platform:

Proficiency in working with Azure data services, including Azure Data Lake Storage, Azure SQL Database, and Azure Blob Storage. Knowledge of Azure Data Factory (ADF), Azure Databricks, Azure Synapse Analytics (formerly SQL Data Warehouse), and other Azure data tools. Data Modeling and Database Design: Experience in designing and implementing data models. Expertise in creating and optimizing databases for performance and scalability. Data Integration: In-depth knowledge of Azure Data Factory for orchestrating and automating data workflows. Integration of diverse data sources into a unified and coherent data ecosystem. Data Transformation and Processing:Proficiency in data transformation using tools like Azure Databricks. Implementation of data processing pipelines for ETL (Extract, Transform, Load) tasks.

• Azure Synapse Analytics: Hands-on experience with Azure Synapse Analytics for large-scale data warehousing and analytics. Design and optimization of data warehouse solutions in Azure Synapse. Big Data Technologies: Familiarity with big data technologies, including Apache Spark for distributed data processing. Implementation of scalable and efficient data processing solutions. SQL and Query Optimization: Strong SQL skills for querying and manipulating data. Optimization of SQL queries for performance.

• Power BI: Proficient in creating interactive and visually appealing reports and dashboards using Power BI. Integration of Power BI with various data sources for real-time analytics. Data Security and Compliance: Implementation of data security measures and compliance standards in Azure. Ensuring data privacy and protection in accordance with industry regulations. Data Quality Management: Implementation of data quality checks and validation processes. Monitoring and improving data quality throughout the data lifecycle. Scripting and Programming: Scripting skills, particularly in languages like Python or PowerShell. Automation of data-related tasks using scripting and programming. Collaboration and Communication: Effective communication skills for collaborating with cross-functional teams. Ability to convey technical concepts to non-technical stakeholders.