Support Data Engineering Professional

Key Responsibilities: Python Development: Develop, maintain, and support data engineering solutions using Python with strong coding standards. Data Frames & Data Processing: Work extensively with Data Frames using libraries such as pandas or PySpark for data transformation and analysis. ETL Pipeline Development: Build, enhance, and support reliable ETL pipelines for data ingestion, transformation, and loading from multiple data sources. Data Modeling: Design and maintain data schemas, including relational and dimensional data models, to support analytics and reporting needs. Data Migration: Perform data migration activities between different storage systems, formats, or environments while ensuring data accuracy and integrity. Production Support: Monitor data pipelines, troubleshoot issues, and provide timely resolution to ensure smooth data operations. Data Quality & Optimization: Identify data quality issues, optimize data processing workflows, and improve performance and reliability. Collaboration: Work closely with cross-functional teams such as analytics, application development, and infrastructure to support data requirements. Documentation: Create and maintain technical documentation for data pipelines, models, and migration processes.