Data Engineer Intern

Job Description

About the Role: Data Engineer Intern (Equity Compensation)

We are MedLaunch, a dynamic startup building a transformative healthcare accreditation platform. We are revolutionizing how hospitals and healthcare organizations manage compliance, improve quality, and navigate complex regulatory processes. Our platform leverages cutting-edge technology and deep healthcare domain expertise to solve critical problems nationwide.

The Opportunity

This is a unique opportunity for aspiring Data Engineers to gain significant hands-on experience. Our goal is to convert successful interns into full-time employees, meaning you’ll be entrusted with full-time responsibilities from day one. Operating within a high-velocity growth startup, you’ll be expected to move fast and contribute significantly.

You will work directly with our engineering team on a production-grade healthcare platform, gaining invaluable experience with enterprise systems. Your contributions will directly impact our product and customers, making this a truly impactful learning experience.

Compensation Structure:

While the base position is unpaid, qualified candidates may receive upfront equity compensation based on their experience level and demonstrated capabilities. We evaluate each applicant individually and offer equity packages commensurate with their potential contribution to MedLaunch’s growth and success.

What You’ll Build

As a Data Engineer Intern, you will be instrumental in developing critical components of our platform, including:

• Entity Resolution Systems: Building components for healthcare facility lookup and matching.
• Data Processing Pipelines: Designing and implementing robust ETL workflows for compliance tracking.
• Analytics Systems: Developing data trigger refresh mechanisms and dashboard feeding systems to provide actionable insights.
• ML Pipeline Integration: Creating the essential data infrastructure to support our Machine Learning team’s models.
• Quality Monitoring: Implementing sophisticated data validation and monitoring systems specifically for sensitive healthcare data.

Key Responsibilities

Data Pipeline Development:

• Build efficient AWS Lambda functions for processing APIs and diverse healthcare data sources.
• Develop robust ETL pipelines using Python and SQL for managing complex healthcare compliance data.
• Implement rigorous data validation and quality checks for sensitive healthcare information to ensure accuracy and integrity.
• Create automated data processing workflows for accreditation documents, streamlining operational efficiency.

Database & Storage:

• Work hands-on with MongoDB Atlas and S3 for data storage and management.
• Design and optimize SQL database schemas for performance and scalability.
• Implement S3-based data lake architecture, organizing data into Bronze, Silver, and Gold zones for progressive refinement.
• Build caching systems using Redis for performance optimization to enhance user experience.
• Utilize and configure AWS Athena for interactive analytics and querying large datasets.

Analytics Support:

• Create reliable data feeds for executive dashboards and KPI tracking, providing key business insights.
• Build healthcare-specific analytics and benchmarking data pipelines to measure performance and identify trends.
• Support batch processing systems for calculating and reporting hospital quality metrics.
• Collaborate closely with our ML team to integrate predictive models into data workflows, enhancing platform capabilities.
• Build healthcare-specific analytics and benchmarking data pipelines using Athena for database queries.

Healthcare Compliance:

• Implement stringent HIPAA-compliant data processing and audit trail systems to ensure regulatory adherence.
• Build robust data governance and documentation standards for all data operations.
• Create automated monitoring and alerting systems for data quality issues, ensuring proactive problem resolution.

Required Qualifications

Technical Skills:

• 1-3+ years with Python and SQL: Proven experience in data processing, scripting, and complex database querying.
• 1-2 years AWS Experience: Practical knowledge of Lambda, S3, or other cloud data services.
• Database Knowledge: Experience with SQL databases and/or NoSQL (MongoDB preferred).
• ETL/Data Processing: Solid understanding of data pipeline concepts and batch processing.

Data Engineering Fundamentals:

• Experience with data transformation and cleaning techniques.
• Understanding of data warehousing and data lake concepts.
• Basic knowledge of data quality and validation techniques.
• Familiarity with version control (Git) and collaborative development workflows.

Nice to Have:

• Healthcare or regulated industry experience.
• Experience with Spark, Delta Lake, or distributed computing frameworks.
• Knowledge of data visualization and Business Intelligence (BI) tools.
• Understanding of ML pipeline integration concepts.
• HIPAA compliance knowledge.

Technical Stack

Data Processing:

• Languages: Python, SQL
• Cloud: AWS (Lambda, S3, EMR, Athena)
• Databases: MongoDB Atlas, PostgreSQL, Redis
• Processing: Spark, Delta Lake, batch and stream processing

Integration & ML:

• ML Integration: SageMaker, MLflow (working closely with our ML team)
• Analytics: Athena for scheduled/triggered queries
• Monitoring: CloudWatch, automated data quality checks

Our Hiring Process

We believe in a transparent and thorough selection process that respects your time while ensuring a mutual fit:

1. Initial Screening Call: We’ll discuss your background, experience, and career goals, while providing a detailed overview of the role and our team culture.
2. Technical Challenge: You’ll receive a real-world technical challenge to complete within a specified timeframe. We encourage you to leverage all available resources—including AI tools, documentation, and libraries—just as you would in a production environment. This reflects how we actually work and allows you to showcase your problem-solving approach.
3. Technical Interview: We’ll have an in-depth discussion about your solution and explore related technical concepts. You should be prepared to walk through every aspect of your submission—explaining architectural decisions, code logic, trade-offs, and potential improvements. Whether you wrote the specific code section manually or generated it with AI assistance, you must demonstrate complete ownership and understanding of the entire codebase. This is a production-level assessment: we expect you to discuss, debug, and defend your work as if it were going live tomorrow.

We’re looking for engineers who can think critically, adapt their approach, and truly understand the systems they build—not just those who can generate code.

Ready to apply? We look forward to hearing from you!

MedLaunch is an equal opportunity employer committed to diversity and inclusion.