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Senior Applied Research Data Engineer
Skills
BusinessBusiness IntelligenceCuratingHealthcareInterviewingInterviewing Subject Matter ExpertsLabels
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Experience learning new domains quickly and solving ambiguous data problems
- Comfort working with incomplete documentation and legacy systems
- Ability to interpret what data means before processing it
- Ability to translate conversations with clinicians product experts and researchers into robust data products
- Ability to create documentation data definitions and semantic models that other teams depend on
- Strong focus on data quality reproducibility provenance and research integrity
Day to day
- Build and own reusable gold-layer data products that support AI machine learning and generative AI research
- Transform structured semi-structured and unstructured healthcare data into trusted model-ready datasets
- Investigate and document complex business logic by analyzing source systems stored procedures application code and stakeholder workflows
- Partner with researchers clinicians and product experts to design datasets for experimentation evaluation and model training
- Create semantic definitions lineage documentation provenance records and data quality frameworks for reproducible research
- Develop point-in-time-correct datasets feature sets and evaluation corpora for classical ML and generative AI workloads
- Support advanced AI data preparation techniques including programmatic labeling weak supervision synthetic data generation and research dataset curation
- Serve as a bridge between domain experts researchers and engineering teams turning tacit knowledge into durable data assets