Overview
Principal Data Engineer, hands-on senior technical leader for the design, implementation, optimization, and operation of the Data Hub’s core data platform capabilities.
What you'll do
- Design, build, and maintain scalable, secure, reliable cloud-native data platform infrastructure.
- Develop infrastructure-as-code, CI/CD pipelines, deployment automation, and environment management processes.
- Improve observability with monitoring, alerting, logging, and performance tracking.
- Design and evolve data models and data architecture standards aligned with governance, lineage, security, and regulatory requirements.
- Build and optimize ingestion, transformation, and delivery pipelines across business domains.
- Establish and enforce engineering, GitOps/DevOps, testing, and deployment standards; conduct architecture and code reviews.
- Lead performance optimization and production incident root-cause analysis and remediation.
What you'll need
- 8+ years of experience in Data Engineering, Platform Engineering, Infrastructure Engineering, or related technical disciplines.
- Demonstrated expertise designing and implementing modern cloud-based data platforms.
- Strong hands-on experience with data warehousing, orchestration, transformation, and DevOps technologies.
- Experience building and optimizing large-scale data pipelines and data models.
- Deep understanding of software engineering principles, infrastructure automation, and production operations.
- Experience operating in highly regulated, compliance-sensitive, or enterprise environments.
- Expert-level SQL and strong Python skills.
Nice to have
- Experience within healthcare, life sciences, pharmaceutical, or analytics organizations.
- Experience supporting AI/ML platforms, MLOps capabilities, feature stores, or model deployment frameworks.
- Familiarity with claims, formulary, commercial, or real-world evidence datasets.
- Experience leading enterprise-scale data migrations, modernization programs, or platform consolidations.
- Exposure to global engineering teams and distributed delivery models.