Junior Data Engineer

Job Description

A leading organization is seeking an early-career Data Engineer to support the design, development, and maintenance of data solutions. This opportunity is ideal for recent graduates or professionals with up to two years of experience who have a strong foundation in data engineering, programming, and data pipeline development.

The Data Engineer will work with modern data platforms and technologies to build scalable data workflows, support data integration efforts, and contribute to the overall reliability and accessibility of business data.

Responsibilities

• Design, develop, and maintain data pipelines that support data integration and processing needs.
• Create and optimize SQL queries for data extraction, transformation, and analysis.
• Develop data engineering solutions using Python.
• Support the ingestion, transformation, and movement of data across systems and platforms.
• Collaborate with cross-functional teams to understand data requirements and implement effective solutions.
• Troubleshoot and resolve issues related to data quality, pipeline performance, and data processing workflows.
• Assist with the ongoing enhancement and maintenance of data infrastructure and engineering processes.
• Contribute to data validation, testing, and operational support activities.
• Document data workflows, processes, and technical solutions.

Required Experience and Skills

• 0 to 2 years of experience in data engineering, software development, or a related technical field.
• Strong SQL skills with experience writing and optimizing queries.
• Experience developing solutions with Python.
• Knowledge of data pipeline architecture, development, and maintenance.
• Understanding of ETL and data transformation processes.
• Strong analytical and problem-solving abilities.
• Ability to work with complex datasets and data-driven applications.
• Strong communication and collaboration skills.

Preferred Experience and Skills

• Experience working with Google Cloud Platform (GCP).
• Familiarity with Claude tools.
• Experience supporting cloud-based data engineering environments.
• Knowledge of modern data integration and processing frameworks.

FAQ

1. What does a Junior Data Engineer do?

A Junior Data Engineer supports the development and maintenance of data pipelines that move information from source systems into databases, warehouses, or analytics platforms. The role typically includes data preparation, transformation, validation, troubleshooting, documentation, and assisting senior engineers with production workflows.

2. What level of SQL knowledge is expected from a Junior Data Engineer?

Candidates should be comfortable writing SQL queries to filter, join, aggregate, and transform data. Understanding joins, subqueries, common table expressions, and basic query optimization is useful for working with production datasets.

3. How is Python used in junior data engineering work?

Python can be used for data processing, automation, API integrations, validation scripts, and pipeline development. Junior engineers may also use Python libraries to transform datasets and automate repetitive data-related tasks.

4. What types of data pipelines might a Junior Data Engineer work on?

Typical workloads include batch ETL or ELT pipelines, API-based ingestion, database-to-database transfers, file processing, and scheduled data workflows. Junior engineers often maintain existing pipelines before taking ownership of smaller components or new integrations.

5. Which tools are useful to learn for a Junior Data Engineer role?

Useful technologies include SQL databases, Python, Git, workflow orchestration tools such as Airflow, and cloud data platforms such as AWS, Azure, or Google Cloud. Familiarity with data warehouses, transformation frameworks, or platforms such as Snowflake and Databricks can also be helpful.

6. How does a Junior Data Engineer contribute to data quality?

The engineer helps apply validation checks, investigate inaccurate or missing records, monitor pipeline results, and verify that transformed data meets defined requirements. Following established testing, logging, and documentation practices is important for maintaining trustworthy datasets.