Junior Data Engineer (Entry-Level)

Key Responsibilities

• Assist in designing, developing, testing, and maintaining ETL/ELT data pipelines.
• Collect, transform, and load structured and semi-structured data from multiple sources.
• Support batch and basic real-time data processing workflows.
• Work with technologies such as Python, SQL, PySpark, Apache Spark, Databricks, Snowflake, and dbt.
• Gain hands-on experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Assist with cloud-based data storage, processing, and data integration solutions.
• Work with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Support modern data warehouses and lakehouse platforms such as Snowflake, Databricks, Redshift, BigQuery, or Azure Synapse.
• Assist with data modeling, schema design, and performance optimization.
• Perform data validation, cleansing, troubleshooting, and quality checks.
• Identify and resolve data pipeline failures and data inconsistencies.
• Monitor scheduled data workflows and support reliable data delivery.
• Collaborate with Data Engineers, Data Analysts, Data Scientists, and business teams.
• Use Git for version control and collaborative development.
• Maintain technical documentation for pipelines, data models, and workflows.

Required Skills & Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Software Engineering, Engineering, or a related field.
• Experience: 0–1 year of full-time professional Data Engineering or related experience.
• Technical Skills: Strong foundational knowledge of SQL and relational database concepts; Basic programming knowledge in Python; Understanding of ETL/ELT concepts, data pipelines, data warehouses, and data modeling; Familiarity with one or more technologies such as Spark, PySpark, Databricks, Snowflake, dbt, Airflow, or Kafka; Basic knowledge of at least one cloud platform: AWS, Azure, or GCP; Familiarity with Git, GitHub, branching, merging, and pull requests; Exposure to Linux/Unix commands is a plus.
• Soft Skills: Strong analytical thinking and problem-solving abilities; Good communication and collaboration skills; Attention to detail and an interest in working with large datasets; Strong willingness to learn new data technologies and continuously develop technical skills.