Data Engineer

Requirements:

• Bachelor's degree in Computer Science, Information Technology, Information Systems, Data Science, or a related field from a reputable university.
• Minimum 3 years of experience in Data Engineering, Data Analytics, or a related field, preferably with hands-on data migration experience.
• Strong proficiency in Python, including reading, maintaining, and developing scripts for data processing and automation.
• Advanced SQL skills, including complex joins, data aggregation, data granularity, and query performance optimization.
• Hands-on experience with ETL processes, data pipelines, data integration, and Data Warehouse concepts.
• Experience transforming unstructured and semi-structured data (JSON, XML, and logs) into structured datasets.
• Proficient in processing large datasets from various sources, including Excel, CSV, and databases.
• Good understanding of data migration, cleansing, validation, reconciliation, data governance, and data quality management.
• Familiarity with cloud platforms, particularly Microsoft Azure. Experience with Azure Data Factory (ADF) is a strong advantage.
• Familiarity with dbt and BI tools is an advantage.
• Strong analytical, problem-solving, and troubleshooting skills.
• Excellent English communication skills, both written and verbal.

Job Responsibilities:

• Analyze, maintain, and enhance existing Python scripts for data processing, pipelines, and automation.
• Extract and integrate data from various source systems into centralized platforms and Data Warehouses.
• Develop and maintain ETL workflows to ensure reliable data extraction, transformation, and loading.
• Transform unstructured and semi-structured data (JSON, XML, and logs) into clean, structured, and analysis-ready datasets.
• Process, cleanse, validate, and reconcile data from Excel, CSV, databases, and other sources.
• Develop and optimize SQL queries for data processing, analysis, aggregation, and validation.
• Ensure data accuracy, completeness, consistency, and integrity throughout data migration and integration processes.
• Monitor and troubleshoot data pipelines, investigate discrepancies, and resolve data-related issues.
• Collaborate with business stakeholders and technical teams to understand requirements and deliver scalable data solutions.
• Prepare and maintain technical documentation, data transformation logic, and data quality reports.