Note: The job is a remote job and is open to candidates in USA. CUS Tech is seeking an entry-level Data Engineer to help design, develop, and maintain data pipelines, databases, and data integration processes. The role supports data processing and quality while collaborating with analysts, scientists, and software engineers.

Responsibilities

• Assist in developing, testing, and maintaining data pipelines for collecting, transforming, and processing structured and unstructured data
• Write and optimize SQL queries to extract, manipulate, and validate data from relational databases
• Support the development of ETL/ELT (Extract, Transform, Load) workflows
• Work with Python scripts to automate data processing and repetitive tasks
• Assist in integrating data from multiple sources, including APIs, databases, and external systems
• Perform data cleaning, validation, and quality checks to ensure accuracy and consistency
• Support the maintenance and monitoring of data warehouses and data lakes
• Collaborate with data analysts, data scientists, and software engineers to meet business data requirements
• Document data workflows, pipeline configurations, and technical processes
• Troubleshoot basic data pipeline issues and assist senior engineers in resolving technical problems

Skills

• Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field
• Basic knowledge of SQL and relational databases such as MySQL, PostgreSQL, or SQL Server
• Familiarity with Python programming and basic data manipulation
• Understanding of fundamental data engineering concepts, including ETL/ELT processes and data pipelines
• Basic understanding of data structures, database design, and data modeling
• Familiarity with file formats such as CSV, JSON, and Parquet
• Strong analytical, problem-solving, and troubleshooting skills
• Good communication skills and the ability to work collaboratively in a team environment
• Willingness to learn new technologies and adapt to evolving data engineering practices
• Internship, academic project, or hands-on experience in data engineering, analytics, or database management
• Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP)
• Familiarity with data warehouses such as Snowflake, Amazon Redshift, or Google BigQuery
• Basic knowledge of Apache Spark, Apache Airflow, or similar data processing and orchestration tools
• Understanding of Git and version control systems
• Familiarity with Linux commands and shell scripting
• Exposure to data visualization or business intelligence tools such as Power BI or Tableau
• Knowledge of basic data governance, security, and data quality practices

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