Software Engineer Intern - Data Infrastructure

As a Software Engineer Intern - Data Infrastructure, your main responsibilities will include learning and contributing to our existing event mining framework and data pipelines. You will assist in building and scaling the framework to handle more substantial datasets, implement Spark-based solutions, and support the migration from legacy systems. Participation in research and experimentation to enhance our framework's capabilities will also be a key part of your role.

To qualify for the Software Engineer Intern - Data Infrastructure position, candidates should have proficiency in Python and strong analytical thinking skills. An eagerness to learn is crucial, along with a basic understanding of cloud platforms such as AWS, Azure, or GCP. While not mandatory, familiarity with Spark or other big data technologies would enhance your application.

The internship experience as a Software Engineer Intern - Data Infrastructure offers extensive hands-on learning in a collaborative environment. Interns work closely with seasoned engineers, gaining insights into real-world data processing and cloud technology applications. You can expect to take part in various projects, contributing to significant challenges while enhancing your technical skills and professional competencies.

Interns at our company can look forward to immense learning opportunities through direct work on high-impact projects. You'll gain insights into data engineering best practices, cloud technologies, and big data frameworks while closely collaborating with experienced team members. The internship allows for exploration and experimentation, fostering professional and personal growth throughout.

The hourly pay for the Software Engineer Intern - Data Infrastructure position ranges from $19 to $65, based on factors like your year in school, degree, experience, and location. We strive to offer competitive compensation that reflects your skills and contributions.

When answering this question, highlight any academic projects or personal exercises where you've utilized Python for data analysis, automation, or scripting. Discuss specific libraries you've used, such as Pandas or NumPy, to demonstrate your hands-on experience.

In your response, briefly define cloud computing and mention any platforms you've worked with, like AWS, Azure, or GCP. Discuss how you have utilized cloud services for projects, such as storing data, running applications, or deploying machine learning models.

Frame your answer around a specific project where you encountered data difficulties, such as handling missing values or optimizing processing time. Explain the steps you took to resolve the issue and the outcome, highlighting your analytical thinking and perseverance.

Discuss practices such as writing unit tests, conducting code reviews, and maintaining clear documentation. Emphasize the importance of testing data integrity and performance optimizations to ensure quality in data pipelines.

Begin by defining what Spark is as a big data processing framework. Share any experience you have with Spark, such as using it for distributed data processing, real-time data analysis, or machine learning projects, and explain how it improved the performance of your work.

Express your enthusiasm for the company's focus on data infrastructure and innovation. Mention specific aspects of their work that excite you, such as their projects or technology stack, and how you believe this internship aligns with your career goals.

Share a systematic approach you take, such as online courses, hands-on practice through projects, or engaging with community forums. Emphasize your adaptability and commitment to continuous learning in the ever-evolving tech landscape.

Choose a relevant example of a team project, detailing your role and how you contributed to group success. Highlight communication, collaboration, and conflict resolution skills, demonstrating your ability to thrive in team settings.

Discuss specific techniques, such as exploratory data analysis using visualization tools, statistical analysis, or data cleaning procedures. Emphasize your methodical approach and any relevant tools you have experience with.

Explain your organizational strategies, such as the use of task management tools or prioritizing based on project urgency and impact. Share examples to illustrate your ability to manage time effectively under pressure.