Data Engineering Intern

Data Engineering We are hiring on behalf of one of our clients for a Data Engineering opportunity in Australia. This opportunity is open to students, recent graduates, and professionals who want to work with data infrastructure, build dependable data workflows, and contribute to the systems that power analytics and business applications. Position: Data Engineering Intern Location: Australia | Remote Role Overview In this role, you will help turn data from different sources into structured, usable information. You will be involved in data ingestion, transformation, storage, quality checks, and workflow automation while working alongside teams that rely on accurate and accessible data. Core Areas Data ingestion and transformationPipeline design and maintenanceDatabase and warehouse operationsData quality managementAutomation and cloud-based workflows Your Responsibilities Build and maintain workflows that move data between operational systems, databases, and analytical environments.Connect data from APIs, files, applications, databases, and other business sources.Develop SQL queries for data extraction, transformation, reconciliation, and analysis.Assist in creating reliable ETL and ELT processes for scheduled and recurring workloads.Transform raw information into structured datasets suitable for reporting and analytical applications.Monitor scheduled pipelines and investigate failures, incomplete loads, and data processing issues.Carry out validation checks to identify unusual records, missing information, duplicates, and inconsistencies.Support the organisation of tables, schemas, and datasets within relational database environments.Contribute to data warehouse and data lake development according to project requirements.Help improve pipeline efficiency by reviewing query performance and processing workflows.Assist with integrating data across different business platforms and technical environments.Maintain technical documentation covering schemas, data flows, transformations, and dependencies.Support the automation and scheduling of repeatable data engineering processes.Work with analysts and data science teams to understand how datasets need to be prepared and delivered.Investigate data-related technical issues and assist with resolving pipeline and database problems.Contribute ideas for improving data availability, reliability, scalability, and maintainability. Essential Qualifications Knowledge of databases, data structures, and fundamental data engineering concepts.Practical understanding of SQL and relational database systems.Familiarity with Python or another programming language used in data workflows.Understanding of ETL/ELT processes and data transformation.Basic knowledge of APIs and methods of moving data between systems.Awareness of data quality, validation, and consistency principles.Strong logical thinking and troubleshooting abilities.Comfortable working with technical documentation and structured information.Ability to work collaboratively with engineering, analytics, and technical teams. Technical Advantage Exposure to Python tools such as Pandas, PySpark, or similar libraries.Familiarity with Spark and distributed data processing.Experience or coursework involving AWS, Azure, or Google Cloud.Knowledge of platforms such as Snowflake, BigQuery, or Amazon Redshift.Understanding of orchestration tools such as Apache Airflow.Familiarity with Docker or other container technologies.Experience using Git for version control and collaborative development.Awareness of streaming technologies such as Kafka.Knowledge of data modelling and database performance techniques.Understanding of data security, governance, and privacy practices.Professional proficiency in English; additional languages are an advantage. What This Opportunity Offers Remote exposure to data engineering projects supporting organisations in Australia.Hands-on experience with data ingestion, transformation, storage, and pipeline workflows.Opportunities to work across databases, cloud environments, and modern data technologies.Experience supporting the infrastructure behind analytics and data-driven applications.Exposure to different data sources, architectures, and engineering challenges.Opportunity to develop stronger programming, SQL, and data engineering capabilities.Project experience that can add value to a professional Data Engineering portfolio.