Qamar Ul

About Qamar Ul

I am a seasoned QA/QE professional with over 12 years of experience in software testing…

Experience

1. Principal - Qa Engineering

   BigRio

   Aug 2025 — Present · US

   End-to-End Test Strategy: Architect and lead the implementation of the QA strategy for the entire data pipeline, encompassing source data ingestion, ETL/ELT processes, and final data loading into the research data warehouse (OMOP CDM).Test Automation Framework: Develop and maintain a scalable, containerized test automation framework (e.g, using Python, Pytest, Docker) to validate data at each stage.Data Integrity and Validation:Schema and Structural Validation: Automate checks for schema drift and data type consistency.Implement scripts to perform checksum validations between source and target systems.Semantic Validation: Develop complex SQL queries and data comparison scripts to ensure the accuracy of transformations (e.g, verifying that a CPT code is correctly mapped to its corresponding procedure in the transformed data).De-identification Validation:PHI Detection Testing: Design and execute tests the PHI de-identification and NLP models, using a mix of real and synthetically generated data with known PHI patterns.False Positive/Negative Analysis: Develop metrics and automated reports to track the rate of false positives (over-redaction) and false negatives (missed PHI), and collaborate with the data engineering team to refine the de-identification algorithms.NLP and Clinical Terminology Validation:NER and Entity Linking: Create test harnesses to validate the performance of Named Entity Recognition (NER) models against annotated clinical notes, verifying the accuracy of entity extraction and the mapping to standardized terminologies like SNOMED CT and RxNorm.Code Mapping: Validate the automated mapping of clinical text to CPT and ICD-10 codes, ensuring a high degree of precision and recall.Technical Leadership and Mentorship: Serve as the technical authority on testing methodologies, review code, and provide guidance to other QA and data engineers on best practices for building robust and reliable data systems.