Data Engineer

Level: Mid-level to senior, based on the required 5+ years of hands-on Azure Databricks and Spark experience.

Summary:
This role is focused on building and supporting large-scale data pipelines using Azure Databricks, Spark, Python/PySpark, and SQL. The candidate should have strong hands-on engineering experience with production data processing, data lake architecture, ETL, data quality, and Azure analytics services. This is a technical delivery role that also requires strong documentation and communication skills.

Main Responsibilities:

• Build and implement data pipelines using Azure Databricks and Spark.
• Write optimized Python/PySpark and SQL code for very large, TB-scale data processing.
• Design and maintain data lake architecture and ETL workflows.
• Manage data quality, integrity, and validation.
• Use Azure data services for storage, processing, and analytics.
• Create technical documentation, requirements, and testing documents.
• Collaborate with business and technical teams to understand requirements and deliver solutions.
• Use Git for version control.

Must-Have Skills:

• 5+ years of hands-on experience with Azure Databricks and Spark.
• Strong Python/PySpark and SQL coding skills.
• Experience optimizing performance for TB-scale data processing.
• Strong understanding of data lake architecture and ETL processes.
• Experience with Azure data analytics services.
• Data quality management experience.
• Strong technical documentation skills.
• Ability to clearly communicate technical concepts.
• Git experience.

Most Important Fit Criteria:
The strongest candidates will have production-level Azure Databricks and Spark experience, strong PySpark and SQL skills, and a proven ability to build and optimize large-scale data pipelines. Prioritize candidates who have worked with TB-scale data, data lakes, ETL, and data quality in Azure environments.

Nice-to-Have:

• Experience working across cross-functional teams.
• Strong requirements gathering and testing documentation experience.
• Prior experience supporting production data platforms.