Data Engineer (Middle/Senior)

Key Responsibilities:
Design, develop, and optimize scalable data pipelines and ETL processes to ingest, transform, and load large volumes of structured and unstructured data from various sources into our data warehouse.
Build and maintain data integration workflows using tools and technologies such as Apache Spark, Apache Airflow, Kafka, and AWS Glue.
Implement data models, schemas, and architectures to support business analytics, reporting, and data visualization requirements.
Collaborate with data scientists and analysts to understand data requirements, perform data wrangling and preprocessing, and support machine learning model development and deployment.
Stay current with emerging technologies and best practices in data engineering, big data processing, and cloud computing to drive continuous improvement and innovation.
Objectives:
Data Infrastructure Excellence. Design, build, and manage a cutting-edge data infrastructure that ensures scalability, reliability, and security to meet current and future business demands.
Data Integration and Insight Generation. Develop and maintain state-of-the-art ETL processes and analytics models that integrate data from diverse sources, ensuring high data accuracy and timeliness, and providing actionable insights for strategic decision-making.
Collaboration, Innovation, and Continuous Improvement. Foster a culture of innovation and continuous improvement in data practices, collaborating closely with key stakeholders to leverage data in driving business solutions and shaping the company's data and analytics strategy.