Job Description
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Company Overview
A healthcare technology company focused on generating high-accuracy real-world evidence (RWE) for clinical, regulatory, and reimbursement applications. Its platform integrates heterogeneous real-world data sources, including electronic health records (EHR), with data science and artificial intelligence to support complex clinical studies. The company works with major biopharmaceutical organizations and operates an AWS-centric technology environment spanning data processing, AI, workflows, and analytics.
Responsibilities
- Lead the design and evolution of data platform architecture, establishing technical patterns and standards for scalable data engineering.
- Build and operate production-grade data pipelines that reliably ingest and transform high-variance real-world clinical data at scale.
- Design automated pipelines capable of detecting problems, recovering gracefully, and surfacing issues without requiring constant manual intervention.
- Contribute to quarterly data product releases while collaborating with product, clinical, and customer success teams to meet delivery commitments.
- Develop data quality tests that reflect the evolving requirements of downstream data consumers.
- Mentor and support other data engineers through code reviews, architectural decisions, and shared engineering standards.
- Actively use and advocate for AI tools that improve development velocity and code quality.
- Collaborate across engineering, product, and clinical teams to ensure technical decisions account for the broader business and domain context of the data.
Qualifications
- 8+ years of experience in data engineering, including experience at a technical lead level.
- Production experience with Snowflake and dbt as primary data platform technologies.
- Strong Python skills for developing and maintaining production data pipelines.
- Experience building resilient pipelines for irregular and high-variance data sources.
- Strong understanding of systems design, including observability, failure recovery, automation, and operational reliability.
- Ability to understand and incorporate business and domain context into data engineering decisions.
- Experience actively using AI tools in engineering workflows and interest in applying AI to data quality monitoring and large-scale anomaly detection.
- Strong communication skills and the ability to collaborate effectively with engineering, product, and clinical stakeholders.
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