Software Engineer - Data Processing
Job description
About the role
This position designs and implements data processing pipelines and large scale distributed systems for healthcare data. The team solves complex distributed data issues within legal constraints and supports the full software development lifecycle. Collaboration with product managers, clinical informaticists, architects, and other engineers drives the daily work and strategic direction. You will own the end to end lifecycle of data processing components from initial requirements through production deployment and monitoring. The role requires balancing innovative technical solutions with strict adherence to healthcare regulations and data governance policies. You will translate ambiguous product needs into robust technical designs that ensure correctness, security, and performance at scale. Strong written and verbal communication is essential to articulate technical decisions to both technical and non-technical stakeholders.
Key facts
What you'll do
Implement and support systems that process unprecedented volumes of healthcare data while operating within legal and regulatory boundaries.
Collaborate with product managers, clinical informaticists, architects, and engineers to align solutions with cross functional objectives.
Apply creative problem solving and rapid learning in the healthcare domain to resolve complex production issues in distributed systems.
Review data specifications and manage large scale data storage and distribution using specialized protocols.
Debug and resolve complex production issues in distributed systems using systematic investigation and root cause analysis.
Write production quality, efficient, multi threaded code that runs reliably in cloud environments and meets stringent performance targets.
Demonstrate proven experience with cloud native architectures and DevOps practices, preferably on Azure but relevant with AWS or GCP experience.
Manage the full data processing lifecycle including ingestion, transformation, validation, storage, and secure delivery to downstream consumers.
Partner closely with data scientists and analytics teams to ensure pipelines support advanced analytics, reporting, and machine learning workflows.
Champion operational excellence by implementing monitoring, alerting, and runbooks that maintain system reliability and incident response.
Contribute to architectural discussions and design reviews to ensure scalability, maintainability, and alignment with enterprise standards.
Mentor junior engineers by providing code review feedback, sharing best practices, and promoting a culture of learning and quality.
Participate in on call rotations to address urgent production incidents and improve system resilience through proactive improvements.
Drive continuous improvement by identifying bottlenecks, refactoring legacy components, and adopting new technologies where appropriate.
Requirements
Hold a Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Systems, or a related field (advanced degree a plus).
Bring 3+ years of professional software engineering experience, including designing, building, and operating distributed systems at scale.
Write production quality, efficient, multi threaded code that runs reliably in cloud environments.
Review data specifications and manage large scale data storage and distribution using specialized protocols.
Debug and resolve complex production issues in distributed systems.
Demonstrate proven experience with cloud native architectures and DevOps practices, preferably on Azure but relevant with AWS or GCP experience.
Possess authorized work eligibility in the United States because sponsor work visas are not available for this position.
Earn a Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Systems, or a related field as the minimum academic credential.
Commit to adhering to healthcare regulations and data governance policies that protect patient privacy and data integrity.
Consistently communicate technical concepts clearly and effectively to both technical and non technical audiences.
Practical notes
Employment authorization in the United States is required because sponsor work visas are not available.
The role follows a hybrid work model with regular team activities that may occur virtually and in person.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Work in health technology centers on data platforms that enable research and clinical decision support.
Cloud native development and DevOps practices underpin scalable healthcare data systems.
Cross functional collaboration is common with clinicians, engineers, and product teams.
Opportunities exist for professional growth through training and development programs.
Roles in data processing often handle sensitive information and require strong attention to reliability and compliance.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.