GS – Data Science Pod

Data Engineer, Data and Platform Engineering

Hybrid Role (downtown Indy 3x / week)

Compensation: $60 - $75

Join a global healthcare leader dedicated to uniting caring with discovery to make life better for people around the world. As part of the Digital Health organization, the Data and Platform Engineering team transforms wearable and sensor data into trusted, analysis-ready datasets that power digital health studies and clinical trials. As a Data Engineer, you will play a pivotal role in building and maintaining the pipelines, automation, and quality systems that connect digital health technologies (DHT) to scientists and clinical teams, directly impacting how disease is measured and new medicines are evaluated.

You will collaborate with study teams from the earliest stages of study design, ensure the quality and traceability of data, and support the integration of AI and advanced analytics into the digital health platform. This is an opportunity to contribute to research and innovation in digital health technologies, with a focus on automation, data integrity, and scalable infrastructure.

• Support early-stage design of digital health technology (DHT) studies by collaborating with study teams to understand study objectives, physiological signals of interest, and practical data collection requirements.
• Build, operate, and maintain scalable data pipelines for wearable and sensor data, ensuring reliable data flow from collection to analysis-ready datasets.
• Monitor and track the flow of DHT and clinical trial data through pipelines, identifying and addressing issues such as device malfunctions, wear compliance, and emerging data quality problems.
• Develop and maintain automated data quality check systems, performing root-cause analysis and ensuring data traceability, reproducibility, and compliance with regulatory standards.
• Manage data storage and organization for raw and processed data, maintaining secure, efficient, and well-documented access across teams and replicating data as needed.
• Serve as a primary developer for pipeline code, including data movement, processing, aggregation, quality control, and integration of AI capabilities.
• Build and maintain internal platforms and APIs to expose curated pipeline data to AI tools and assistants used across the team.
• Configure and manage compute infrastructure that supports AI-driven research, model development, and large-scale data processing.
• Participate in model or algorithm development activities, including work on novel wearables, sleep detection, 3D environment reconstruction, and modern compute/GPU approaches for generating insights at scale.
• Contribute to internal discussions and strategy regarding the use of digital health technologies and wearables in both in-clinic and at-home research settings.
• Stay current with relevant academic research and contribute to publications produced by the team.
• Present technical work to internal and external audiences, ensuring clear, audience-appropriate storytelling and effective communication.

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field (Master's or PhD considered); 3-5 years of professional data engineering experience.
• Strong programming skills in Python and SQL, with hands-on experience building and maintaining production data pipelines (ETL/ELT).
• Experience with cloud computing platforms (e.g., Azure, AWS, or GCP) and modern data engineering tools.
• Working knowledge of relational and non-relational database technologies and data modeling practices.
• Demonstrated ability to identify data quality issues and build automated checks to catch and resolve them.
• Experience working with wearable sensor data, time-series data, or other high-frequency data streams.
• Experience building, refining, or product ionizing machine learning models, especially for classification or signal/time-series data (e.g., activity or sleep detection).
• Experience integrating AI or large language models (LLMs) into data platforms or applications.
• Experience in a regulated industry (healthcare, life sciences, or clinical research) is a plus.
• Strong written and verbal communication skills, with the ability to present technical work to both technical and non-technical audiences.
• Collaborative, growth-oriented mindset; willingness to learn new tools, languages, and domain knowledge as needed.

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