Research Data Analyst

We are seeking a highly motivated and skilled Genomic Data Manager to support and advance large-scale genomic research. The successful candidate will play a key role in managing, processing, and analyzing complex genomic datasets and developing scalable, reproducible workflows to facilitate discovery. Working closely with a diverse team of bioinformaticians, statisticians, programmers, and clinical researchers, the Genomic Data Manager will help ensure data quality, accessibility, and integrity across multiple research initiatives.

• Oversee data acquisition, curation, and organization across multiple research projects.
• Maintain data dictionaries, metadata, and documentation consistent with FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
• Strengthen data governance practices to ensure compliance, security, and transparency.
• Collaborate with IT and data infrastructure teams to improve systems that support research data management.
• Work collaboratively with multidisciplinary teams to translate complex data into meaningful insights.
• Communicate findings effectively to both scientific and non-technical audiences.
• Mentor trainees and junior staff to support skill development and knowledge sharing.
• Contribute to the preparation of scientific manuscripts, reports, and presentations

• Bachelor's degree in Computational Biology, Bioinformatics, Computer Science, or related field and at least two years of relevant experience, or an equivalent combination of education and experience.
• Five years of experience in R, Python, or Perl is required
• Three years of experience with Linux/Unix environments, HPC or cloud computing, and source control (Git)
• Four years of hands-on experience with NGS data analysis and workflow management tools (e.g., Nextflow, Snakemake).
• Design, develop, and maintain modular computational pipelines for genomic data validation.
• Optimize workflows for scalability and efficiency in high-performance computing (HPC) or cloud environments.
• Implement best practices in reproducibility through version control (Git) and containerization (e.g., Docker, Singularity).
• Participate in benchmarking and continuous improvement of bioinformatics tools and infrastructure
• Assist with bioinformatics and statistical analyses on large genomic and clinical datasets.
• Conduct quality control, data validation, and reproducibility checks.
• Assist investigators with genomic data queries and provide analytical and technical guidance.
• Apply rigorous analytical approaches to ensure robust and unbiased results