A seasoned data engineer with strong knowledge and hands-on experience on clinical genomic data analytics, knowledge management, data engineering, cloud computing, as well as data platform design and development.

QUALIFICATIONS:

• Education:
  • Master's or higher degree in Data Science, Computer Science/Engineering, Biomedical engineering, or related field with five (5) years of experience in genomic data analysis and software development.

• Experience:

• • Hands-on experience in human genetics/multi-omics data analysis and software development especially in next-generation sequencing data
  • Hands-on experience in clinical genotype and phenotype data analysis and modeling
  • Hands-on experience in data ETL, data management, and data sharing for genomic and clinical applications, especially for next generation sequencing data
  • Hands-on experience with databases, including SQL and no-SQL
  • Hands-on experience with scripting language, such as Bash and Python
  • Hands-on experience in cloud platform (Azure) and commercial data platforms (data lakehouse/data warehouse)
  • Familiarity with commercial/open-source data analytical platforms
  • Familiarity with advanced data visualization techniques
  • Familiarity with clinical actionable variant interpretation and classification
  • DevOps experience such as unit testing, CI/CD is a plus.
  • Strong curiosity and the ability to learn quickly and adapt to a fast-changing environment

DUTIES AND RESPONSIBILITIES:

• Serves as the SME in clinical genomic data analysis, ETL, management and engineering. Provides hands-on support towards building company’s next-generation data analytical platform
• Designs, develops, evaluates, and deploys state-of-the-art analytical solutions to gain valuable data insights based on the genetical, phenotypical, and clinical datasets
• Evaluates, organizes, and manages company’s valuable data assets
• Designs cloud-based data model/schema to efficiently manage and query large scale genomic dataset
• Supports both internal and external data requirements by leveraging AI/ML and GenAI capabilities to keep up with the increasing demands of the business
• Collaborates in a multidisciplinary and regulated clinical diagnostics environment with geneticists, bioinformaticians, software engineers, and IT infrastructure professionals

PHYSICAL DEMANDS AND WORK ENVIRONMENT:

• Frequently required to sit
• Frequently required to stand
• Frequently required to utilize hand and finger dexterity
• Frequently required to talk or hear
• Frequently required to utilize visual acuity to operate equipment, read technical information, and/or use a keyboard
• Occasionally exposed to bloodborne and airborne pathogens or infectious materials