Principal Technology Strategist, Data Analytical Systems

Principal Technology Strategist, Data Analytical Systems

Reports to: SVP, Laboratory Systems and Product Engineering

Work Model: Remote (with periodic travel to San Carlos, CA and Austin, TX)

Position Summary

The Principal Technology Strategist for Data Analytical Systems is an enterprise technical leader responsible for the architectural vision of the "Dry Lab" informatics ecosystem. This role defines how we process, analyze, and interpret massive-scale genomic data—transforming raw sequencing output into life-saving clinical insights.

This strategist owns the long-term technical roadmap for Production Bioinformatics Pipelines and Tertiary Analysis Systems. You will architect the platforms that handle primary/secondary NGS analysis, variant annotation, clinical classification, and the complex orchestration of Clinical Report Writing. Your mission is to ensure our analytical systems are computationally efficient, scientifically rigorous, and compliant with global regulatory standards (FDA/CAP/CLIA).

In the first 90 days, you will conduct a deep-dive audit of our current bioinformatics infrastructure and reporting workflows. You will identify bottlenecks in processing latency and manual "scientist-in-the-loop" steps within the tertiary analysis phase. You will establish a working rapport with the Bioinformatics R&D and Clinical Operation teams to align on a unified "Production-Grade" vision.

Within the first year, you will have architected a scalable, cloud-native framework for "plug-and-play" bioinformatics pipelines that supports our entire portfolio (Oncology MRD/CGP, ECD, and Women’s Health). Success will be defined by a measurable reduction in computational cost-per-sample, increased automation in variant classification, and a modernized reporting workflow engine that significantly reduces the time from "Sequence-Complete" to "Report-Issued."

Key Responsibilities

Production Bioinformatics & Pipeline Orchestration

• Define the enterprise strategy for Production-Grade Bioinformatics, ensuring pipelines are robust, reproducible, and version-controlled for clinical use.

• Architect high-performance computing and/or cloud-native orchestration layers that can scale elastically with laboratory volume.

• Drive "Build-vs-Buy" decisions for pipeline workflow managers (e.g., WDL, Nextflow, Snakemake) and secondary analysis acceleration technologies (e.g., DRAGEN, GPU-based processing).

• Establish standards for data provenance and "Freeze-Down" protocols to ensure regulatory traceability across pipeline versions.

Tertiary Analysis & Clinical Interpretation

• Enable seamless integration between the "Dry Lab" results and “Wet Lab” QC metrics to deliver both operational and scientific insights to reviewers and lab directors.

• Lead the architectural design of Tertiary Analysis Platforms that integrate variant calling with large-scale knowledge bases for annotation and classification.

• Partner with R&D and Clinical Ops to automate the Variant Classification workflow (ACMG/AMP guidelines), reducing manual curator burden through intelligent software design.

• Architect the Clinical Reporting Engine, ensuring it can handle complex multi-omic data, longitudinal results (MRD & CGP), and dynamic template requirements for various global markets.

Data Strategy & Analytics Infrastructure

• Ensure data architectures comply with global privacy standards (HIPAA, GDPR) while maintaining the high availability required for clinical operations.

• Partner with the Lab Software Engineering leaders to ensure a seamless "hand-off" of data between the Wet Lab (LIMS) and the Dry Lab (Bioinformatics).

Regulatory Excellence & "Validation-at-Scale"

• Architect systems that support Continuous Validation, allowing for rapid pipeline updates without compromising clinical integrity or audit readiness.

• Lead the technical narrative for Data Analytical Systems during FDA (PMA/510k) submissions and CAP/CLIA inspections.

• Act as a mentor to the engineering and bioinformatics teams, fostering a culture of software engineering excellence within a scientific domain.

Qualifications

Required

• 10+ years of experience in software architecture or technical strategy, with at least 5 years focused on Bioinformatics, Genomics, or Clinical Data Systems.

• Deep expertise in NGS Data Processing (FastQ to VCF) and the computational challenges of high-throughput primary and secondary analysis.

• Proven experience building or scaling Tertiary Analysis/Interpretation systems, knowledgebases (genetics and/or oncology), and clinical report-writing workflows.

• Exceptional ability to bridge the gap between Geneticists and Software Engineers.

Preferred

• Experience with Oncology diagnostics, specifically MRD (Minimal Residual Disease) or CGP (Comprehensive Genomic Profiling).

• Familiarity with AI/ML integration into clinical decision support systems.

• Advanced degree (Masters or PhD) in Genetics, Computational Biology, or Computer Science.

Work Model & Travel

This is a remote role. Periodic travel to San Carlos, CA and Austin, TX is expected to collaborate with the R&D and Clinical Lab Ops teams.