Offer summary

Qualifications:

Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, or a related field., Familiarity with programming languages such as Python or SQL and AI frameworks like TensorFlow or PyTorch., Basic understanding of data engineering concepts including ETL processes and data pipelines., Strong analytical skills and a passion for data, technology, and AI..

Key responsibilities:

Test new features and validate the functionality of data pipelines and AI models.

Develop and automate testing suites for data engineering workflows and AI applications.

Collaborate with data engineers and AI specialists to identify opportunities for leveraging machine learning tools.

Troubleshoot and resolve issues in data workflows and automation processes.

Job description

#WeAreTradeStation

Who We Are:

TradeStation is an online brokerage firm seeking to level the playing field for self-directed investors and traders, empowering them to claim their individual financial edge. At TradeStation, we're continuously pushing the boundaries of what's possible, encouraging out-of-the-box thinking and relentless search for innovation.

What We Are Looking For:

The Data Engineering Intern will contribute to the development, testing, and automation of data pipelines and workflows within our data engineering and AI ecosystems. This role offers the opportunity to work with cutting-edge technologies, including machine learning and AI tools, while developing scalable and automated solutions. The intern will collaborate closely with the team to ensure high-quality deliverables, implement AI-driven enhancements, and apply best practices in data engineering and AI integration.

What You’ll Be Doing:

Test new features and validate the functionality of data pipelines, AI models, and engineering solutions
Develop and automate testing suites for data engineering workflows and AI-driven applications to ensure reliability and performance
Assist in designing and implementing frameworks, fixtures, and best practices for data engineering and AI solutions
Contribute to the development and deployment of AI models and algorithms within data pipelines
Collaborate with data engineers and AI specialists to identify opportunities for leveraging machine learning and AI tools
Troubleshoot and resolve issues in data workflows, AI models, and automation processes

The Skills You Bring:

Strong analytical and problem-solving skills with a passion for data, technology, and AI
Familiarity with one or more programming languages (e.g., Python, SQL, or similar) and AI frameworks or libraries (e.g., TensorFlow, PyTorch, or scikit-learn)
Basic understanding of data engineering concepts such as ETL processes, data pipelines, or big data tools
Exposure to AI concepts, including machine learning, natural language processing, or computer vision
Ability to work collaboratively in a team environment and take initiative on tasks
Interest in learning about AI integration in data engineering, automation, and best practices

Minimum Qualifications:

Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Artificial Intelligence, or a related field
Experience with programming languages such as Python, SQL, or similar
Basic knowledge of data engineering concepts, including ETL processes, data pipelines, and databases
Familiarity with AI or machine learning concepts and libraries (e.g., TensorFlow, PyTorch, scikit-learn)

Desired Qualifications:

Experience with big data tools and platforms, such as Apache Spark, Hadoop, or Databricks
Familiarity with cloud platforms like AWS or Azure
Hands-on experience with version control systems (e.g., Git)
Exposure to MLOps practices, including deploying and monitoring machine learning models
Experience with data visualization tools (e.g., Tableau, Power BI) or Python libraries (e.g., Matplotlib, Seaborn)
Understanding of data governance, security, and compliance practices

Required profile