Data Engineering Intern

Skydrop; San Clemente (CA); On-site; USD 27,552 - 41,328; Part time

Job summary

Skydrop is looking for a Data Engineering Intern based in San Clemente, California to support the development of scalable data systems for their machine learning platform. The intern will work with large-scale datasets and collaborate with engineers to enable advanced analytics.

This internship provides a unique opportunity to gain hands-on experience building production-ready data systems in a dynamic environment.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in a relevant technical field.
• Hands‑on experience with Python and SQL.
• Exposure to data pipelines or ETL processes.

Responsibilities

• Build and optimize ETL pipelines for data processing.
• Develop data workflows using Python, SQL, and AWS.
• Collaborate with engineers to prepare datasets for machine learning.

Skills

Python programming

SQL/database fundamentals

Data engineering concepts

Problem-solving capabilities

Effective communication

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

AWS

PostgreSQL

Docker

Job description

TDK SensEI is transforming how sensor data is collected, processed, and leveraged powering intelligent, data-driven decision-making across industrial environments. As a pioneer in automated machine learning for edge devices and a subsidiary of TDK Corporation, a global leader in sensor technology, SensEI operates at the forefront of industrial AI and analytics.

The Data Engineering Intern supports the development of scalable data systems and infrastructure that power our machine learning–based equipment monitoring platform. In this role, the intern will work with large-scale sensor datasets, cloud-based technologies, and modern data pipelines, while collaborating closely with software and machine learning engineers to enable advanced analytics and AI applications.

This internship provides hands-on experience in building production-ready data systems within a fast-paced, innovative environment.

KEY RESPONSIBILITIES

• Build, maintain, and optimize ETL/ELT pipelines for processing sensor and operational data
• Develop data workflows and automation using Python, SQL, and AWS services
• Support data ingestion, transformation, validation, and monitoring processes
• Work with structured and semi-structured data from cloud and edge-based systems
• Collaborate with software and ML engineers to prepare datasets for analytics and machine learning models
• Assist with integration and optimization of OLTP and OLAP systems
• Troubleshoot pipeline issues and contribute to improvements in data reliability and performance

ADDITIONAL RESPONSIBILITIES

• Create and maintain documentation, including data flows, system diagrams, and technical specifications
• Participate in code reviews and adhere to engineering best practices
• Support initiatives to improve data quality, observability, and operational efficiency
• Contribute to continuous improvement of data infrastructure and workflows

Other Duties

• Perform other related duties and ad hoc projects as assigned to support departmental and organizational goals
• Workplace Safety: Maintain awareness of and follow all workplace safety guidelines and promote a culture of well-being
• Quality and Compliance: Ensure work is performed in accordance with established quality control and assurance processes
• Ethics and Integrity: Adhere to the company’s values and code of conduct and uphold the highest standards of honesty, integrity, and ethical behavior in all business activities

QUALIFICATIONS

Education/Experience

• Currently pursuing a Bachelor’s or Master’s degree in: Computer Science, Data Engineering, Information Systems, or a related technical field

• Hands‑on experience with Python and SQL
• Exposure to data pipelines, ETL/ELT processes, or data warehousing concepts
• Familiarity with relational and/or analytical databases (e.g., PostgreSQL, MySQL, Redshift)
• Exposure to cloud platforms, preferably AWS

Knowledge/Skills/Abilities

• Strong foundation in Python programming and SQL/database fundamentals
• Basic understanding of data engineering concepts, including pipelines, transformation, and storage
• Familiarity with cloud computing and distributed systems
• Analytical mindset with strong problem‑solving capabilities
• Ability to quickly learn new tools, technologies, and frameworks
• Strong attention to detail and commitment to data accuracy and quality
• Effective communication skills, both written and verbal
• Ability to collaborate in cross‑functional team environments

Preferred / Bonus Qualifications

• Experience with workflow orchestration tools (e.g., Apache Airflow)
• Familiarity with AWS services such as S3, Lambda, Glue, or Redshift
• Exposure to Docker, Linux, or shell scripting
• Understanding of OLTP vs. OLAP systems
• Experience with data lakes, data warehousing, or analytics platforms
• Interest in machine learning and data‑driven systems
• Experience with Git or version control systems