Data Governance Specialist

Technical Skills & Technologies Languages & Query

• SQL- data profiling, validation rules, quality checks, large-scale dataset analysis
• Python - automation, ML/AI techniques, scripting, pipeline development Data Quality & Governance
• Data quality frameworks (Great Expectations, dbt tests) augmented with ML/LLM
• Data governance practices (metadata management, lineage review, issue tracking)
• Data stewardship, remediation workflows, and automated quality monitoring pipelines
• Data lineage and impact analysis
• Data completeness and reliability measurement ML/AI for Data Quality
• Supervised learning - classification models to flag bad records (e.g., mislabeled diagnosis codes, incorrect attributions)
• Unsupervised learning - clustering to detect outliers, unusual patterns, and grouping anomalies in clinical data
• Anomaly detection - model-driven (Isolation Forest, LOF, One-Class SVM)
• Data drift monitoring - statistical process control (KS test, PSI, Jensen-Shannon divergence)
• NLP / Text Analytics - parsing unstructured clinical notes, extracting entities, detecting coding inconsistencies
• Record linkage / Entity resolution
• ML-based deduplication using probabilistic matching (e.g., patient matching across systems)
• Pattern recognition and root cause analysis AI Frameworks & Libraries
• scikit-learn - anomaly detection, clustering, classification
• PyOD - outlier detection
• Tensor. Flow / Py. Torch - deep learning for complex pattern recognition in large clinical datasets
• LLMs (GPT, Claude, etc.) - automated root cause summarization, natural language quality reports, code review assistance Dash boarding & Reporting
• Alerting and monitoring dashboards (Tableau, Power BI, Streamlit, or similar)
• Data quality trend reporting and operational risk communication Healthcare Domain Knowledge
• Healthcare coding systems (ICD, CPT, HCPCS)
• Member attribution logic
• Clinical quality measures (HEDIS, Stars)
• Interoperability data (HL7, FHIR)
• Payer/provider/clinical/population health datasets