Stealth Startup
Data Engineering Intern
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Data Engineering
We are hiring on behalf of one of our clients for a Data Engineering opportunity in the UK. This role is suited to students, recent graduates, and professionals who are interested in building reliable data systems, developing data pipelines, and supporting data-driven applications.
Position: Data Engineering Intern
Location: UK | Remote
Overview
As a Data Engineering Intern, you will contribute to the processes that collect, transform, organize, and make data available for analytical and operational use. You will work with databases, data pipelines, cloud platforms, and data processing workflows to support reliable and accessible data environments.
Key Areas of Work
- Data pipeline development
- Data integration and transformation
- Database and warehouse management
- Data quality and reliability
- Cloud data infrastructure
Key Responsibilities
- Collect and integrate data from databases, APIs, files, applications, and other sources.
- Develop and maintain data pipelines for moving and transforming information between systems.
- Write SQL queries to extract, join, transform, and validate data.
- Build and support ETL and ELT workflows for recurring data processes.
- Clean and structure datasets for analytical and operational use.
- Monitor data pipelines and investigate processing failures or unexpected results.
- Perform data quality checks to identify missing, duplicated, inconsistent, or inaccurate records.
- Work with relational and non-relational databases to organize and manage data.
- Support data warehouse and data lake development and maintenance.
- Assist with integrating data from multiple business and technical systems.
- Optimize queries and data workflows to improve processing efficiency and reliability.
- Document data pipelines, schemas, transformation logic, and technical processes.
- Assist with scheduling and automating recurring data processing tasks.
- Work with analysts, data scientists, and software teams to understand data requirements.
- Troubleshoot data pipeline and database issues with relevant technical teams.
- Contribute to improvements in data architecture, pipeline performance, and data accessibility.
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Required Qualifications
- Understanding of fundamental data engineering and database concepts.
- Working knowledge of SQL and relational databases.
- Familiarity with Python or another programming language used for data processing.
- Understanding of data structures, transformation, and basic ETL concepts.
- Familiarity with data cleaning and validation techniques.
- Basic understanding of APIs and data integration workflows.
- Strong problem-solving and analytical skills.
- Good attention to detail when working with data and technical processes.
- Ability to document technical work clearly.
- Ability to work effectively with data and engineering teams.
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Preferred Technical Skills
- Experience with Python libraries such as Pandas or PySpark.
- Familiarity with Apache Spark or other distributed data processing technologies.
- Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Knowledge of data warehouses such as Snowflake, BigQuery, or Amazon Redshift.
- Understanding of Airflow or other workflow orchestration tools.
- Familiarity with Docker and containerization concepts.
- Knowledge of Git and collaborative software development workflows.
- Exposure to Kafka or other data streaming technologies.
- Understanding of data modelling and database optimization.
- Familiarity with data governance, security, and privacy principles.
- Professional working proficiency in English is required; additional European languages are an advantage.
What You Can Expect
- Remote opportunity supporting data engineering initiatives in the UK.
- Practical exposure to real-world data pipelines and engineering workflows.
- Opportunity to work with SQL, Python, databases, cloud platforms, and data processing tools.
- Experience contributing to data integration and transformation projects.
- Exposure to modern data architectures and technical use cases.
- Opportunity to strengthen practical data engineering and programming skills.
- Projects that can contribute to a professional Data Engineering portfolio.
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