Data Engineering Intern

Bright Network Consulting; Canada; Remote; CAD 25,000 - 39,000; Part time

Job summary

Bright Network Consulting is hiring a Data Engineering Intern for a client in Canada. The role focuses on building, maintaining, and optimising data infrastructure and pipelines.

You will work with structured and unstructured datasets and collaborate with data scientists, analysts, and engineers to support business and analytical needs. Ideal candidates have a strong interest in data engineering, database management, and scalable data processing, with experience in Python, SQL, and cloud

Qualifications

• Strong fundamentals in data engineering and databases.
• Proficiency in Python and SQL.
• Familiarity with ETL/ELT concepts and data pipelines.
• Understanding of data warehousing and data lakes.
• Knowledge of cloud platforms and distributed processing.

Responsibilities

• Design, develop, and maintain scalable data pipelines.
• Build and optimise ETL/ELT workflows from multiple sources.
• Process and transform raw data into usable formats.
• Develop and maintain databases, data warehouses, and storage.
• Collaborate with data scientists, analysts, and engineers.
• Monitor pipelines for accuracy, performance, and reliability.
• Troubleshoot data processing issues and bottlenecks.
• Design efficient data models and database schemas.
• Implement data validation and quality checks.
• Work with cloud data platforms and storage services.
• Maintain technical documentation for pipelines and schemas.

Skills

Python

SQL

Relational databases

ETL/ELT

Data structures

Cloud basics

APIs

Git

Tools

Apache Spark

Apache Hadoop

Airflow

AWS/Azure/GCP

Docker

BigQuery/Snowflake

Job description

We are hiring a Data Engineering Intern for one of our clients in Canada. We are looking for motivated and technically inclined individuals who are interested in building, maintaining, and optimising data infrastructure and pipelines. The ideal candidate should have a strong interest in data engineering, database management, data integration, and large-scale data processing, along with the ability to work with structured and unstructured datasets.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines to support data collection, processing, and transformation.
• Build and optimise ETL (Extract, Transform, Load) and ELT workflows for integrating data from multiple sources.
• Collect, process, clean, and transform raw data into structured and usable formats.
• Develop and maintain database systems, data warehouses, and data storage solutions.
• Work with structured and unstructured datasets to support business and analytical requirements.
• Implement data integration processes to consolidate information from different platforms and systems.
• Monitor data pipelines and workflows to ensure accuracy, consistency, and reliability.
• Identify and troubleshoot data processing issues, pipeline failures, and performance bottlenecks.
• Assist in designing and implementing efficient data models and database schemas.
• Collaborate with data scientists, data analysts, and software engineering teams to understand data requirements.
• Support the development and maintenance of data architecture and infrastructure.
• Implement data validation and quality checks to ensure data integrity.
• Optimise database queries and data processing workflows for improved performance.
• Assist in automating data ingestion, transformation, and processing activities.
• Work with cloud-based data platforms and storage services to manage data infrastructure.
• Maintain technical documentation for data pipelines, database structures, and engineering processes.
• Follow data security, governance, and privacy standards while handling sensitive information.
• Research emerging data engineering technologies and recommend improvements to existing workflows.

Required Skills & Qualifications

• Strong understanding of data engineering fundamentals and database concepts.
• Proficiency in Python programming and SQL.
• Knowledge of relational databases and database management systems.
• Understanding of ETL and ELT processes and data pipeline development.
• Familiarity with data structures, data modelling, and database optimisation.
• Basic understanding of data warehousing and data lakes.
• Knowledge of data cleaning, transformation, and integration techniques.
• Familiarity with APIs and data ingestion processes.
• Understanding of cloud computing and distributed data processing concepts.
• Strong problem-solving, analytical, and logical reasoning abilities.
• Good communication and collaboration skills.
• Attention to detail and ability to work with large datasets.
• Ability to work independently and collaborate effectively in a remote working environment.

Preferred Qualifications

• Experience working on academic, personal, or professional data engineering projects.
• Familiarity with Apache Spark, Apache Kafka, or Hadoop.
• Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Understanding of workflow orchestration tools such as Apache Airflow.
• Familiarity with data warehousing solutions such as Snowflake, BigQuery, or Amazon Redshift.
• Knowledge of Docker and containerisation concepts.
• Understanding of distributed systems and big data technologies.
• Familiarity with version control systems such as Git.
• Experience with data modelling and database schema design.
• Understanding of data governance, security, and quality management practices.

What You Will Gain

• Exposure to real-world data engineering projects and industry-related challenges.
• Practical experience in developing and maintaining data pipelines.
• Opportunities to strengthen programming, database management, and data processing skills.
• Hands-on exposure to modern data engineering tools and technologies.
• Understanding of cloud-based data infrastructure and data warehousing solutions.
• Opportunities to collaborate with data scientists, analysts, and engineering professionals.
• Experience applying data engineering concepts to practical business use cases.