We are looking for an experienced Lead Cloud / Data Architect to design and evolve a cloud-native platform for large-scale genomic analysis supporting the full lifecycle of NGS data, from FASTQ ingestion through variant interpretation, clinical reporting, and long-term storage. The platform supports Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), Gene Panels, MultiOmics, and AI-powered genomic analysis. This is a hands-on technical leadership role focused on building secure, scalable, and cost-efficient data infrastructure that supports the full lifecycle of genomic datasets.
You will define the architecture for storing, managing, and accessing genomic data while working closely with engineering and domain experts to ensure the platform is ready for future workloads, including long-read sequencing, RNA, MultiOmics, and AI-driven applications.
What you’ll do
- Design cloud-native architecture for large-scale genomic data platforms.
- Define storage strategies across the entire data lifecycle, from ingestion to archival and deletion.
- Build scalable, secure, and high-performance data infrastructure in cloud environments.
- Design and implement data tiering strategies (hot / warm / cold / archive).
- Define retention, archival, and deletion policies for genomic datasets.
- Optimize storage costs while maintaining efficient data access.
- Design scalable cloud architecture for NGS analysis platforms supporting WGS, WES, Gene Panels, and MultiOmics workloads.
- Design storage architecture for genomic data from FASTQ ingestion through VCF generation, interpretation, reporting, and archival.
- Define cloud-native architecture supporting AI/ML-enabled genomic analysis and future platform scalability.
- Design integrations with laboratory and healthcare systems (e.g., LIMS, EHR, APIs).
- Ensure data provenance, validation, integrity, and security across the platform.
- Design infrastructure that meets regulatory and compliance requirements.
- Scale data platforms to support growing genomic and bioinformatics workloads.
- Collaborate with engineering teams on architecture decisions and technical direction.
Must have
- Extensive experience as a Cloud Architect, Data Architect, or Lead Data Engineer building large-scale cloud data platforms.
- Deep expertise in cloud architecture and large-scale data infrastructure.
- Experience designing cloud infrastructure for large-scale NGS or genomic data platforms
- Experience designing data lifecycle strategies, including:
- data tiering (hot / warm / cold / archive / Glacier)
- retention policies
- archival strategies
- deletion policies
- Experience optimizing both storage cost and data access performance.
- Strong knowledge of data provenance, validation, security, and governance.
- Experience designing highly scalable cloud platforms.
- Experience with object storage architectures for very large genomic datasets.
- Experience designing cloud platforms supporting AI/ML or data-intensive scientific workloads.
- Understanding of hybrid, private cloud, and public cloud deployment models.
- Hands-on architecture experience in modern cloud environments (AWS, GCP, or Azure).
Domain expertise
We are looking for someone who understands not only bioinformatics processing but also how genomic data should be stored, managed, secured, and accessed throughout its lifecycle.
- Strong understanding of NGS workflows, including FASTQ → Variant Calling → VCF → Interpretation → Clinical Reporting.
- Familiarity with WGS, WES, Gene Panels, structural variants, CNVs, and genomic annotation pipelines.
- Understanding of scalable bioinformatics pipelines and cloud-native genomic workflows.
Nice to have
- Experience integrating with LIMS, EHR, or laboratory automation systems.
- Experience with clinical reporting or precision medicine platforms.
- Familiarity with HIPAA, GDPR, or other healthcare regulatory requirements.
- Experience working in one or more of the following domains: Biotechnology, Pharma, Clinical Genomics, Diagnostics, Clinical Trials
- Experience building platforms supporting: Long-read sequencing, RNA sequencing, MultiOmics, AI-powered genomics applications
What you’ll bring
- Strong architectural thinking combined with hands-on technical expertise.
- Ability to make long-term platform design decisions.
- Experience balancing scalability, performance, security, compliance, and infrastructure cost.
- Excellent collaboration and communication skills in cross-functional engineering teams.