About This Role
This hybrid role combines Analytics Engineering with hands-on data analysis, focusing on developing data foundations, infrastructure, and analytical tools for the Aidoc Clinical Value team. You will translate complex analytical and clinical needs into scalable, reliable, and reusable data solutions, designing data models, pipelines, analytical layers, and tools that enable complex Clinical Value studies across products and customers.
Responsibilities
- Design, build, and maintain the data infrastructure supporting Clinical Value analyses, including reusable data models, pipelines, analytical layers, and tools
- Own the technical and analytical implementation of complex Clinical Value applications, adapting established methodologies to different products, customers, and real-world data environments
- Develop complex analytical solutions into reusable and scalable frameworks, enabling broader and more efficient execution across the team
- Identify data-quality issues, technical bottlenecks, and repetitive processes, and develop solutions and automation to improve reliability and efficiency
- Work closely with analysts, Data Engineering, and other technical teams to translate analytical needs into effective, maintainable data solutions and establish best practices
- Lead selected hands-on data-analysis projects, analyzing complex clinical and operational datasets to generate credible, actionable conclusions
- Collaborate with Go-to-Market teams, including Customer Success, to understand customer needs and support impactful customer-facing value demonstrations
Requirements
- B.Sc. or higher in a Scientific or Engineering discipline with 3+ years of experience in Analytics Engineering, Data Engineering, or a technical analytics role
- Advanced Python and SQL skills
- Hands-on experience designing and building production data models, pipelines, and analytical layers
- Experience with orchestration/transformation tools (DBT, Airflow, or similar) and cloud data platforms (Databricks, Snowflake, BigQuery, or AWS)
- Strong understanding of data modeling, data quality, testing, and maintainability best practices
- Hands-on experience integrating AI development tools and agents (e.g., Cursor, Claude agents, GitHub Copilot) into daily workflows to accelerate coding, debugging, and pipeline development
- Strong analytical mindset with the ability to independently explore and analyze complex datasets, summarize findings, and extract meaningful insights as needed
- Demonstrated ability to work independently, manage tight timelines, and translate ambiguous analytical needs into robust technical frameworks
- Excellent communication skills for bridging technical and non-technical stakeholders
Nice to have:
