Azure Data Engineer - ETL/Data Warehousing in Synapse

Bluelightconsulting; Mar del Plata; A distancia; ARS 132.725.744 - 176.967.658; Jornada completa

Descripción de la vacante

Bluelight is seeking an ETL Data Engineer to join our expanding data team. You will design, develop, and maintain data integration pipelines using Python (PySpark) and Azure Synapse Analytics to deliver accurate analytics data.

You will collaborate with data scientists and analysts to ensure data quality and availability, applying best practices in data governance, modeling, and performance tuning. Hybrid work may be offered depending on project needs.

Formación

• Bachelor's degree or equivalent work experience; data certifications are a plus.
• Proven ETL data engineering experience with Python (PySpark) for REST APIs, SQL tables, and CSVs.
• Proficiency with Azure Synapse Analytics resources: Notebooks, Pipelines, Linked Services, Key Vault.
• Strong SQL skills; SparkSQL and MS SQL for data extraction, transformation, and loading.
• Experience with Git/Azure DevOps and data integration best practices.

Responsabilidades

• Develop and maintain ETL processes using Python (PySpark) in Azure Synapse notebooks/pipelines.
• Design data warehousing structures and star schemas for scalable analytics.
• Extract data from REST APIs, SQL databases, and CSV files; ensure data quality and integrity.
• Collaborate with data architects to model data aligned to business needs.
• Monitor pipelines, diagnose issues, and optimize performance to meet SLAs.

Conocimientos

Python (PySpark)

ETL

SQL

Data Integration

Azure Synapse

Power BI

Educación

Bachelor’s degree in CS/IT or related field

Herramientas

Azure Synapse Analytics

Git/Azure DevOps

Tableau

Descripción del empleo

Bluelight is seeking an ETL Data Engineer to join our expanding data team. You will design, develop, and maintain data integration pipelines using Python (PySpark) and Azure Synapse Analytics to deliver accurate analytics data.

You will collaborate with data scientists and analysts to ensure data quality and availability, applying best practices in data governance, modeling, and performance tuning. Hybrid work may be offered depending on project needs.