Domain Consultant

Azure Data Engineer – Job Description

Main Roles and Responsibilities

1. Design, develop, and implement ETL pipelines for the continuous deployment and integration.
2. Collaborate with other data engineers and data scientists to understand data requirements and optimize data solution processes.
3. Assure that data is cleansed, mapped, transformed, and otherwise optimised for storage and use according to business and technical requirements
4. The ability to automate tasks and deploy production standard code (with unit testing, continuous integration, versioning etc.)
5. Load transformed data into storage and reporting structures in destinations including data warehouse, high speed indexes, real-time reporting systems and analytics applications
6. Build data pipelines to collectively bring data together
7. Other responsibilities include extracting data, troubleshooting and maintaining the data warehouse
8. Implement best practices for pipeline building and governance.
9. Troubleshoot and resolve issues related to pipeline deployment and performance.
10. Ensure compliance with security and data privacy standards
11. Solution design using Microsoft Azure services and other tools

Required skills and experience

• 7-10 years of experience in Azure Data Analytics, Synapse, DevOps.
• Proficiency in programming languages such as Python, SQL, Py-Spark, Spark-SQL.

• Experience with Azure: ADLS, Synapse, Stream Analytics, SQL DW, Analysis Services, Serverless Architecture, ARM Templates

• Experience with cloud platforms like Azure, AWS or and their respective services.
• Knowledge of CI/CD pipelines, automation tools and version control systems like Git.
• Strong problem-solving skills and ability to troubleshoot complex issues.
• Experience with monitoring tools.
• Ability to work collaboratively in cross-functional teams.

Nice to have:

• Should have Data Engineer certification from any discipline – Azure, AWS.
• Azure Synapse Studio, Data bricks, Fabric
• Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.)

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