A data engineer intern plays a crucial role in helping companies build and maintain their data infrastructure. As an intern, you will gain hands-on experience working with large datasets, building data pipelines, and using tools like SQL, Python, Spark, and cloud platforms like AWS.
Typical responsibilities
Some of the day-to-day responsibilities of a data engineer intern may include:
- Building and optimizing data pipelines & ETL processes
- Creating database schemas and data models
- Writing SQL queries to transform and load data into databases and data warehouses like Redshift, Snowflake, BigQuery etc.
- Building processes for data modeling, data mining, and segmentation
- Supporting software engineers by providing clean, accurate data
- Monitoring data quality and ensuring validity of data
- Troubleshooting issues with data pipelines and databases
- Automating manual processes around data storage and processing
- Experimenting with new tools and technologies like Apache Spark, Kafka etc.
Must-have skills
To succeed as a data engineering intern, here are some of the key technical skills you’ll need:
- SQL – SQL skills are mandatory for manipulating, analyzing, and extracting insights from large datasets. You’ll need to be fluent in writing complex SQL queries across databases like MySQL, PostgreSQL, Hive etc.
- Python – Python is the most popular language for data engineering and you’ll need to be able to write Python scripts to build pipelines, workflows, and integrations.
- Data modeling – Understanding how to model relational and NoSQL database schemas is crucial for organizing and querying data.
- ETL processes – Building pipelines for extracting, transforming and loading data from diverse sources into destinations like data warehouses, lakes etc.
- Cloud platforms – Experience with cloud providers like AWS, GCP, Azure and their managed data services is a huge plus.
- Spark – Knowing framework like Spark helps when processing and analyzing big data.
- Airflow – Being able to use workflow schedulers like Airflow to manage pipelines is useful.
- Git – Version control with Git is essential for collaboration and DevOps.
Valuable experiences
Here are some of the valuable experiences you can gain as a data engineering intern:
- Working with massive datasets requiring distributed, scalable systems
- Building end-to-end data pipelines handling data ingestion, processing, and analysis
- Using cloud platforms like AWS, GCP for data storage, processing, and analytics
- Getting real-world experience with technologies like Hadoop, Spark, Kafka, Airflow etc.
- Learning how data helps drive business decisions and strategy
- Collaborating with software engineers, data scientists, and analytics teams
- Understanding how companies leverage data as a competitive advantage
- Gaining exposure to data security, compliance, governance, and ethics
- Using agile methodologies within fast-paced data teams
- Building a solid data engineering foundation for your career
Day to day tasks
While specific day-to-day tasks depend on the company, here is an example of what a data engineering intern’s schedule may look like:
| Time | Task |
|---|---|
| 9:00 – 10:00 AM | Standup meeting with team to discuss priorities and blockers |
| 10:00 – 11:00 AM | Work on ETL pipeline to load user analytics data into Redshift |
| 11:00 – 12:00 PM | Write Apache Spark code to analyze event logs and track usage |
| 12:00 – 1:00 PM | Lunch break |
| 1:00 – 2:00 PM | Meet with manager to discuss internship progress and goals |
| 2:00 – 3:30 PM | Build PostgreSQL data model and entities for new product feature |
| 3:30 – 4:00 PM | Debug issue with Airflow DAG failing |
| 4:00 – 5:00 PM | Work on automation script to refresh development environment |
Essential soft skills
In addition to technical abilities, data engineering interns need these soft skills:
- Communication – Clearly explain your work and ideas to teammates and stakeholders.
- Collaboration – Work with others like data scientists and analysts on shared goals.
- Creativity – Find innovative ways to approach data problems.
- Analytical thinking – Pay attention to details and think critically about data.
- Time management – Prioritize tasks and work efficiently on tight timelines.
- Learning mindset – Continuously pick up new technical knowledge and skills.
What you’ll learn
As a data engineering intern, you’ll get hands-on experience and learn:
- How companies build robust, scalable data pipelines and infrastructure
- How to work with massive distributed datasets and systems
- How to use data technologies like Spark, Kafka, Airflow, cloud platforms etc.
- The role data plays in driving business decisions and products
- How data teams work cross-functionally with engineers, scientists, analysts etc.
- How to clearly present technical concepts and your work to others
- Professional software engineering practices around version control, code reviews etc.
- Valuable hard skills and experiences for your resume and career
Future career paths
A data engineering internship can open up several fantastic career directions such as:
- Data Engineer – Build and optimize data infrastructure at technology companies or enterprises.
- Analytics Engineer – Support analytics applications and data science teams with pipelines and platforms.
- Database Administrator – Manage database systems and ensure smooth operations.
- Data Ops Engineer – Combine software engineering and data skills for deploying and monitoring production data systems.
- Solutions Architect – Design technology solutions that incorporate data platforms, infrastructure, and analytics.
The experience you gain as an intern provides an invaluable foot in the door for these exciting and well-compensated careers.
Finding data engineering internships
Many technology companies and startups hire data engineering interns. Here are some tips for finding opportunities:
- Look for openings at tech companies like Airbnb, Netflix, Uber, LinkedIn, Salesforce etc.
- Search job boards like Indeed, Glassdoor, AngelList, VentureLoop etc.
- Browse internship listings on LinkedIn and set job alerts
- Leverage your university’s career center and alumni network
- Attend career fairs and tech events to network
- Reach out directly to companies and ask about internship opportunities
- Check companies’ websites for internship programs
- Follow data teams and professionals on LinkedIn and Twitter
How to stand out as an applicant
To get noticed for competitive data engineering internships, do these things:
- Highlight relevant coursework, skills, and experience on your resume
- Work on data projects using tools like SQL, Python, Spark etc. to showcase your abilities
- Share data projects on GitHub or technical blog to demonstrate your work
- Do online courses and certifications (ex. edX) to build your skills if lacking experience
- Prep for technical questions related to data modeling, SQL, ETL, data pipelines etc.
- Practice for interviews by doing mock coding challenges
- Show passion and interest in the role and company during interviews
- Ask smart, thoughtful questions to your interviewers
- Participate in hackathons and data challenges to solve real-world problems
- Get involved with data community through meetups, conferences etc.
Conclusion
A data engineering internship provides incredible hands-on experience and foundational knowledge to kickstart your career. You’ll get exposure to real-world large scale data problems and how businesses leverage data and infrastructure. By honing technical abilities like SQL, Python, data modeling, ETL, and cloud platforms while developing valuable soft skills, a data engineering internship equips you with skills and experiences highly valued across the tech industry for years to come.
