Gene Solutions offers genetic testing services for cancer detection, precision therapy, and reproductive health. It uses Next-Generation Sequencing and AI-driven analyses across its global network of seven NGS laboratories, including two CAP-accredited facilities, to deliver assays such as non-invasive prenatal testing (NIPT), ctDNA-based cancer monitoring, and comprehensive genomic profiling. The company positions itself as a service-based provider, serving hospitals and clinics with advanced diagnostic and monitoring solutions across Asia. Since 2017, it has processed over 2.2 million tests and works with more than 4,500 healthcare providers, expanding its reach in Asia. Core technologies include AI, Genomics, NGS, and ctDNA-based assays.
Gene Solutions offers genetic testing services for cancer detection, precision therapy, and reproductive health. It uses Next-Generation Sequencing and AI-driven analyses across its global network of seven NGS laboratories, including two CAP-accredited facilities, to deliver assays such as non-invasive prenatal testing (NIPT), ctDNA-based cancer monitoring, and comprehensive genomic profiling. The company positions itself as a service-based provider, serving hospitals and clinics with advanced diagnostic and monitoring solutions across Asia. Since 2017, it has processed over 2.2 million tests and works with more than 4,500 healthcare providers, expanding its reach in Asia. Core technologies include AI, Genomics, NGS, and ctDNA-based assays.
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About us
Gene Solutions, Vietnam's leading biotech company founded in 2017, pioneers genetic testing and AI-driven healthcare solutions. With a growing network of next-generation sequencing (NGS) labs across Asia and ambitious global expansion plans, we're creating an intelligent platform that provides personalized insights, risk predictions, and clinical tools to revolutionize pregnancy care. Join us in making a real difference for millions of families worldwide.
About the Role
As a Data Engineer, you will design, build, and maintain scalable data pipelines that integrate NGS, clinical, pregnancy, and operational data from multiple sources. You will work closely with AI engineers, bioinformaticians, data scientists, clinicians, and product teams to ensure data is accurate, traceable, validated, and ready for modeling and reporting.
Key Responsibilities
Design, build, and maintain reliable ETL/ELT data pipelines for clinical, laboratory, and NGS-related data.
What We're Looking For
Bachelor’s degree in Computer Science, Data Engineering, Bioinformatics, Biomedical Engineering, or equivalent experience.
Strong proficiency in Python for data processing, validation, exploratory analysis, and basic modeling.
Solid experience with SQL and relational or analytical databases (e.g., PostgreSQL, BigQuery, DuckDB).
Why Join Us
Competitive package including 13th-month salary, performance bonus, full social insurance on gross salary, private health insurance, annual health check-up, and company trips.
Opportunity to work with large-scale real-world pregnancy and genomic datasets.
Direct impact on improving maternal and prenatal healthcare outcomes across Asia and beyond.
Collaborative environment where data science, engineering, and clinical
expertise work closely together.
A culture that values scientific rigor, curiosity, and ownership—your analysis will matter.
Ingest and integrate data from heterogeneous sources (LIMS, EMR/EHR extracts, CSV/Excel, APIs, databases).
Clean, normalize, and standardize real-world medical and pregnancy data with high missingness and inconsistency.
Define and maintain canonical schemas, data dictionaries, and standardized feature definitions.
Implement automated data validation rules (completeness, consistency, range, temporal logic).
Build data quality monitoring, logging, and alerting for production pipelines.
Ensure data lineage, traceability, and auditability to support clinical research and compliance needs.
Optimize data storage formats, partitioning, and query performance for analytics and AI workloads.
Perform exploratory data analysis (EDA) to assess data quality, distributions, bias, and cohort characteristics.
Build basic features and baseline models (e.g., logistic regression, tree-based models) to validate data signals and support analysis.
Collaborate closely with AI engineers, data scientists, clinicians, and operations teams to deliver analysis-ready, trustworthy datasets.
Hands-on experience building, operating, and maintaining production-grade data pipelines end-to-end.
Experience with data processing libraries such as Polars or Pandas.
Familiarity with workflow orchestration or scheduling tools (e.g., Airflow, Prefect, Dagster, cron).
Prior experience working with medical, clinical, biotech, or life-sciences data.
Understanding of real-world healthcare data challenges (missing data, inconsistent coding, bias).
Strong ownership mindset with attention to data quality, correctness, and reproducibility.
Ability to communicate clearly with both technical teams and clinical or operational stakeholders.
