DP-203: Data Engineering on Microsoft Azure 2025

Based on the job description you've provided, it seems you are looking for a candidate with a comprehensive skill set in data engineering, specifically within the Microsoft Azure ecosystem. The role requires expertise in designing and implementing scalable and robust data solutions, managing and processing large datasets, implementing security measures to protect sensitive data, and monitoring and optimizing data storage and processing systems.

Here's a breakdown of the skills and knowledge areas that are crucial for this position:

1. Data Ingestion and Processing:

   • Expertise in designing ETL pipelines and data ingestion strategies.
   • Proficiency with Azure Data Factory or Apache Airflow for scheduling and automating data movement tasks.
   • Experience with Apache Spark for large-scale data processing, including handling of streaming data, batch processing, and real-time analytics.

2. Data Storage:

   • Knowledge of Azure Blob Storage, Data Lake Storage, and Databricks.
   • Ability to design schemas for data storage solutions like Delta Lake or Parquet files.
   • Understanding of data partitioning and columnar storage for performance optimization.

3. Data Security:

   • Designing security measures for data encryption both at rest and in transit, including managing keys and secrets.
   • Implementing Azure RBAC, ACLs, row-level, and column-level security to protect sensitive data.
   • Designing privacy and compliance strategies aligned with industry standards (GDPR, HIPAA, etc.).

4. Data Management:

   • Experience with data cleaning, transformation, and enrichment.
   • Knowledge of handling interruptions, upsert operations, and replaying archived stream data.
   • Designing and configuring exception handling for data processing tasks.

5. Monitoring and Optimization:

   • Proficiency in monitoring performance using Azure Monitor, logs, and metrics.
   • Ability to optimize resource management, query performance, and data processing workflows.
   • Troubleshooting failed Spark jobs and pipelines.

6. Development and Deployment:

   • Programming skills in languages such as Python, Scala, or Java for Azure Databricks development.
   • Experience with version control systems like Git for pipeline artifacts.
   • Ability to deploy and manage Spark jobs within a pipeline.

7. Data Querying and Analysis:

   • Proficiency in writing complex SQL queries and leveraging data catalogs.
   • Understanding of analytical patterns and performance optimization techniques.

8. Azure-specific Knowledge:

   • Familiarity with Azure Synapse Analytics, Azure Databricks, and other Azure services relevant to data engineering.
   • Experience with Azure Cosmos DB or Azure SQL Database if applicable to the role's requirements.

9. Soft Skills:

   • Strong problem-solving skills.
   • Ability to work collaboratively in a team environment.
   • Effective communication and documentation skills.

The candidate should have hands-on experience with Azure data services, preferably with a track record of successful projects that can be referenced during the interview process. Additionally, they should be comfortable with both the technical and soft skills necessary to thrive in a fast-paced, collaborative environment.