A Data Engineer is responsible for designing, building, and maintaining the data infrastructure and pipelines that enable an organization to collect, store, and analyze large volumes of data.

They play a crucial role in ensuring data is accessible, reliable, and ready for analysis by data scientists, analysts, and other stakeholders.

• Bachelor's degree in computer science, Information Technology, or a related field.

• Proficiency in one or more programming languages (e.g., Python, Java).

• Knowledge of databases, including SQL and NoSQL.

• Experience with ETL processes and tools like AWS Glue.

• Familiarity with big data technologies like Hadoop and Spark.

• Cloud platform experience (e.g., AWS, Azure, GCP), including services like Amazon Redshift.

• Data modeling expertise.

• Experience with data visualization tools like Tableau.

• Version control using tools like Git.

• Problem-solving abilities.

• Strong communication skills for collaboration with team members and stakeholders.

• Attention to detail to maintain data accuracy.

• Adaptability to learn and use new technologies and tools.

• Create and maintain ETL (Extract, Transform, Load) processes to move data from various sources to data storage systems using tools like AWS Glue.

• Manage databases (SQL and NoSQL) to ensure data is stored efficiently and securely, including working with Amazon Redshift.

• Design data models and schemas for efficient data storage and retrieval.

• Implement processes to ensure data accuracy and quality.

• Utilize tools like Hadoop, Spark, and distributed computing to process large datasets.

• Work with cloud providers like AWS, Azure, or GCP to store and process data in the cloud.

• Develop dashboards and reports using visualization tools like Tableau to support data-driven decisions.

• Collaborate with data scientists and analysts to understand data requirements and deliver data solutions.