At PointClickCare, we are building the data foundation that powers the next generation of AI and machine learning products in healthcare. We are seeking a Senior Applied Research Data Engineer who thrives at the intersection of data engineering, applied research, and domain discovery.
This is not a traditional data engineering role focused solely on pipelines and infrastructure. You will work closely with AI researchers, data scientists, clinicians, and product experts to transform complex healthcare data into trusted, reusable, AI-ready research assets. Success in this role requires curiosity, investigative thinking, and the ability to uncover meaning in complex, poorly documented systems.
You will be responsible for learning new domains quickly by reading source code, reverse-engineering SQL and business logic, interviewing subject matter experts, and building durable semantic data products that support experimentation, model development, evaluation, and production AI systems.
The ideal candidate enjoys solving data mysteries, creating order from ambiguity, and building datasets that researchers trust. You understand that the quality, semantics, lineage, and documentation of a dataset are often more important than the model itself.
In this role, you will:
- Build and own reusable gold-layer data products that power 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 directly with researchers to design datasets for experimentation, evaluation, and model training.
- Create semantic data definitions, lineage documentation, provenance records, and data quality frameworks that enable 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.
What makes someone successful in this role:
- You enjoy learning new domains and solving ambiguous data problems.
- You are comfortable working with incomplete documentation and legacy systems.
- You naturally ask "What does this data really mean?" before asking "How do I process it?"
- You can translate conversations with clinicians, product experts, and researchers into robust data products.
- You create documentation, data definitions, and semantic models that other teams depend on.
- You care deeply about data quality, reproducibility, provenance, and research integrity.
Required Skills and Experience:
5+ years building production data systems, with at least 2 supporting ML or AI workloads.
Track record of learning complex new data domains quickly, through reading source code, interviewing experts, and building durable artifacts others rely on.
Advanced Python, SQL, and PySpark/Databricks for working with large, messy data. Expert SQL specifically: comfortable reading complex stored procedures and reverse-engineering business logic from queries.
Databricks ecosystem depth: Delta Lake, Unity Catalog, Spark/PySpark tuning, MLflow.
AI domain literacy: working understanding of embeddings, tokenization, feature engineering, point-in-time correctness, train/validation/test splits, data drift, and the differences between what classical ML and generative models need from data.
Data wrangling across modalities: transforming unstructured content (text, PDFs, transcripts, logs) and structured tabular data into clean, model-ready forms.
AI-friendly data formats (Parquet, Hugging Face datasets) and storage layout decisions — partitioning, sharding, caching, that keep researcher workflows responsive in Azure, AWS or other working environments.
Data quality, filtering, and synthesis pipelines: support for programmatic labeling and weak supervision (e.g. Snorkel or equivalent), near-duplicate detection (MinHash/LSH), content and quality filters, LLM-API-driven synthetic data generation.
Pipeline orchestration (e.g. a la Airflow, Databricks Workflows, Dagster, or Prefect) and dataset versioning including Unity Catalog and feature-store support.
Experience handling regulated or sensitive data under controlled access (HIPAA or equivalent). Familiarity with general de-identification concepts.
Git-based version control and CI/CD for data and code.
Strong written documentation. Skill in eliciting requirements and tacit knowledge from technical and non-technical experts.
Bachelor’s degree in computer science, data science, engineering, statistics, or related field. Equivalent practical experience considered.
Preferred:
Hands-on EHR data experience, ideally in skilled nursing, long-term care, post-acute care, or senior living.
Working knowledge of clinical terminologies (ICD-10, SNOMED CT, LOINC) and data standards (HL7v2, FHIR, CCDA).
dbt for transformation and testing.
Familiarity with training-side ML frameworks (e.g. PyTorch) sufficient to debug data-side bottlenecks; experience supporting LLM or foundation-model training or fine-tuning data pipelines.
Clinical NLP, OCR, document parsing, or ASR / transcript pipeline experience.
Data lineage and catalog tools.
Prior experience embedded inside an AI or ML research team.
Master’s degree in a relevant quantitative or computer science field.
What Success Looks Like
AI researchers can start new projects without spending the opening weeks reconstructing what PointClickCare entities mean or rebuilding the same transformations. The gold datasets they need exist, are versioned, are documented, and accelerate work across EDA, experiments, model development, and evaluation. As coverage expands across data types, modalities, and product surfaces, the function grows with it.
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