Data Engineer Intern

DATA ENGINEERING INTERN
1. Education Requirements
- Completed Bachelor’s degree in one of the following:
- Computer Science
- Computer Software Engineering
- Software Engineering
- Computer Engineering
- Or a closely related technical field
- Minimum GPA: 3.0/4.0
- Preferred GPA: 3.5+/4.0
2. Professional & General Requirements
Previous work or internship experience is preferred, particularly in:
- Data Engineering
- Software Engineering
- Database Engineering
- Data Science
The candidate should demonstrate:
- Strong analytical and problem-solving skills
- Strong attention to detail
- Ability to learn quickly
- Ability to work independently
- Strong written and verbal communication skills
- Reliability and accountability
- Ability to follow technical instructions accurately
- Willingness to work with large and complex datasets
- Ability to document technical work clearly
- Ability to collaborate with:
- Backend Engineers
- Frontend Engineers
- DevOps Engineers
- QA Engineers
- Project Managers
- Data Engineers
- Data Scientists
- Experience with Git/GitHub
- Basic experience with PostgreSQL, MySQL, SQL Server, or another relational database
- Familiarity with Pandas and NumPy
- Basic Linux experience
- Exposure to cloud platforms
- Basic REST API experience
3. Language Requirements
Mandatory
- English — Fluent
4. Technical Requirements
Candidates should have foundational knowledge in the following areas.
A. SQL & Databases
Candidates should understand:
- "SELECT"
- "JOIN"
- "GROUP BY"
- Filtering and conditions
- Aggregations
- Basic query development
- Relational database concepts
- Data validation
- Identifying and troubleshooting data problems
Experience with PostgreSQL, MySQL, SQL Server, or similar relational databases is preferred.
B. Python
Candidates should have basic Python programming skills, including:
- Writing data-processing scripts
- Automating repetitive data tasks
- Reading and processing structured data
- Identifying data-quality problems
- Writing scripts to help clean, validate, or correct data
- Basic debugging
Experience with Pandas and NumPy is preferred.
C. Data Engineering Fundamentals
Candidates should understand the basic concepts of:
- Data extraction
- Data cleaning
- Data transformation
- ETL/ELT
- Structured data
- CSV files
- Database loading
- Data validation
- Data-quality checking
D. REST APIs & JSON
Candidates should have a basic understanding of:
- REST APIs
- API requests and responses
- HTTP fundamentals
- JSON
- Extracting data from APIs
- Processing API data
- Loading API data into databases or other structured formats
E. OpenStreetMap & Overpass Turbo
Familiarity with OpenStreetMap and Overpass Turbo is preferred.
Strong candidates may have experience with:
- Querying OpenStreetMap data
- Overpass Turbo queries
- Extracting geographic data
- Extracting business/location data
- Processing large geographic datasets
Previous experience with business or geographic data extraction is a strong advantage.
F. Git & GitHub
Candidates should have basic experience with:
- Git
- GitHub
- Repositories
- Commits
- Branches
- Pull requests
- Reviewing and managing code changes
G. Linux
Basic Linux/Unix experience is preferred, including familiarity with:
- Command-line tools
- File and directory management
- Running Python scripts
- Basic shell commands
- Working with data files from the command line
H. Cloud Platforms
Exposure to at least one cloud platform is preferred, such as:
- AWS
- Google Cloud
- Microsoft Azure
- Other comparable cloud platforms
Professional cloud experience is not required for this internship.
5. AI Tools
The candidate should be comfortable using modern AI-assisted development and data-engineering tools, including:
- ChatGPT Pro
- Claude Pro
AI tools may be used for:
- SQL development and troubleshooting
- Python scripting
- Debugging
- Data cleaning and transformation
- Deduplication and data validation
- API development and troubleshooting
- Technical research
- Documentation
- Developing data-processing solutions
- Understanding unfamiliar technical concepts
AI Code Verification Requirement
Candidates must not blindly rely on AI-generated code.
They must be able to:
- Read and understand AI-generated code
- Identify potential errors
- Test generated code
- Verify results
- Modify the code when necessary
- Understand the technical consequences of using the generated solution
The ability to effectively use AI while independently verifying its output is an important part of this position.
6. Data Quality & Business Data
Because the position involves working with large business datasets, candidates should be comfortable working with:
- Large CSV files
- Business records
- Geographic/business data
- Duplicate records
- Missing data
- Invalid data
- Inconsistent data
- Data normalization
- Data validation
- Data matching and deduplication
Experience cleaning, deduplicating, matching, or validating business data is a strong advantage.
7. Team Collaboration
The Data Engineering Intern will work as part of a larger technical organization and may collaborate with:
- Data Engineers
- Data Scientists
- Backend Engineers
- Frontend Engineers
- DevOps Engineers
- QA Engineers
- Project Managers
The candidate must be able to communicate technical issues clearly, follow established technical procedures, document work, and coordinate effectively with other team members.
8. Candidate Profile
The ideal candidate is a recent Computer Science, Software Engineering, Computer Engineering, or related graduate who has a strong technical foundation and wants to develop practical experience in data engineering, large-scale data processing, APIs, databases, and business-data systems.
The position is intended for an entry-level candidate. Candidates do not need to be experts in every technology listed above, but they should have strong fundamentals, demonstrate the ability to learn quickly, and be capable of independently solving progressively more complex technical problems.

Work arrangement
Yes