Data Engineer

Nezda Global; Metro Manila; Vor Ort; PHP 1.400.000 - 2.200.000; Vollzeit

Zusammenfassung

Nezda Technologies, Inc. is seeking an L3 Data Engineer with extensive Azure data experience, Python, PySpark, and Power BI to build scalable data solutions.

You will design and maintain data pipelines, work with data warehouses, and enable analytics and reporting for business stakeholders.

The ideal candidate has 8–10 years in data engineering, strong Azure skills, and hands-on SQL, Python, PySpark, and BI tooling. This senior role offers a fast-paced tech environment.

Qualifikationen

• 8–10 years of professional data engineering experience.
• Strong hands-on expertise with Microsoft Azure.
• Proficiency in Python, PySpark, and data warehousing.
• Experience with Power BI for analytics.

Aufgaben

• Develop and support Azure-based data engineering solutions.
• Build data pipelines using Python and PySpark.
• Work with data warehouses and data management processes.
• Prepare data for business intelligence and analytics.
• Create reports and visuals with Power BI.

Kenntnisse

Azure

Python

PySpark

Power BI

Data Warehousing

Data engineering

Tools

SQL

Azure Data Factory

Jobbeschreibung

We are looking for an L3 Data Engineer who has extensive experience working with Azure-based data solutions, Python, PySpark, Data Warehousing, and Power BI.

The ideal candidate should have a solid technical background in data engineering and experience working with modern data platforms and analytics technologies.

Key Responsibilities

• Work with Azure-based data engineering technologies.
• Develop and support data engineering solutions using Python and PySpark.
• Work with data warehouse environments and data management processes.
• Support data preparation and processing for business intelligence and analytics.
• Work with Power BI for data visualization and reporting.

Must-Have Qualifications

• 8–10 years of professional experience in Data Engineering.
• Strong experience with Microsoft Azure.
• Hands-on experience with Python.
• Hands-on experience with PySpark.
• Experience in Data Warehousing.
• Experience with Power BI.

Good-to-Have

• Experience working across enterprise-level data environments.