Azure Data Engineer in Globant

Role & responsibilities

Job Summary: As an Azure Data Engineer, you will design, develop, and implement data solutions using Azure ecosystem. Your role will involve bridging traditional data warehousing with modern Lakehouse environments, utilising your expertise in SQL, Azure Databricks, Spark, and OneLake.

Key Responsibilities:

• Design and develop scalable Python applications for data ingestion using a class-based, object-oriented architecture (master controller / services / helpers pattern), applying encapsulation, polymorphism, and dependency injection to produce maintainable, extensible code.
• Expert-level Python (OOP) with deep hands-on experience building production applications: class hierarchies, abstract base classes, polymorphism, encapsulation, and dependency injection.
• Ability to architect and implement master controller/services/helpers application structures.
• Data Engineers not required to implement API; however, they MUST be knowing how to hit RESTFULL API to get data”
• SQL: Strong SQL skills for data querying, transformation, and pipeline validation.
• Azure Data Engineering concepts: Solid understanding of Azure cloud data infrastructure and storage patterns (ADLS, Azure SQL)
• Experience building and maintaining data ingestion pipelines and processing workflows.
• Must have hands-on experience working with Azure Databricks and Azure Data Factory.
• Azure Databricks (ADB) / PySpark: Experience is welcome for high-volume distributed processing scenarios; it is not required for standard ingestion workloads.
• Familiarity with XBRL / iXBRL financial data formats.
• Experience with collaborative code repositories (Git/GitHub) and agile/scrum tooling.
• Ability to work as an independent contributor with minimal supervision.
• Strong sense of ownership and accountability for deliverables. Good interpersonal and communication skills; ability to collaborate effectively with cross-functional teams.
• Strong logical, analytical, and problem-solving skills