Data Engineer III
New
USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 4–6+ years
- Required Skills
- PythonData engineeringCI/CDData modelingDistributed Systems
Requirements
- 4–6+ years of professional software or data engineering experience with a strong focus on building production-grade data systems.
- Experience working with distributed systems, APIs, data pipelines, or cloud-based data infrastructure at scale.
- Strong programming skills in relevant languages such as Python or similar, with experience in data processing and automation.
- Solid understanding of CI/CD, observability practices, and modern software delivery principles.
- Experience designing, testing, and maintaining data pipelines, including unit, integration, and end-to-end testing.
- Strong analytical and problem-solving skills with the ability to debug complex, multi-system data issues.
- Excellent communication skills with the ability to document designs, explain tradeoffs, and collaborate across technical and non-technical teams.
- Bachelor’s degree in Computer Science or related field, or equivalent practical experience.
Responsibilities
- Design, build, and maintain scalable data pipelines and data processing systems that support analytics, reporting, and operational decision-making across a complex healthcare platform.
- Own end-to-end delivery of medium to large data engineering features, from design and implementation through deployment and production support.
- Develop and optimize data models, APIs, and integrations that ensure consistent, high-quality, and accessible data across systems.
- Diagnose and resolve complex data issues spanning multiple services, including performance tuning and root cause analysis of pipeline failures.
- Implement and improve data quality, testing, observability, and CI/CD practices within data workflows.
- Collaborate with product, engineering, and platform teams to translate business requirements into scalable and maintainable data solutions.
- Contribute to technical documentation, design reviews, and post-incident analyses to continuously improve system reliability and engineering practices.
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