Sr Director Software Engineer

Baylor Genetics is seeking a Senior Director of Software Engineering to lead the Bioinformatics Software Engineering team and drive the strategy, architecture, delivery, and operational support of production-grade software platforms that enable clinical genomic diagnostics. This leader will be accountable for building and guiding a high-performing software engineering organization that develops scalable, reliable, validated, and maintainable systems supporting Baylor Genetics’ clinical laboratory, bioinformatics, data, and reporting operations.

This role provides senior technical and people leadership across software engineering, clinical bioinformatics workflow implementation, data integration, platform modernization, and production support. The Senior Director will set engineering priorities, establish standards for software quality and delivery, and ensure that scientific innovation, regulatory expectations, and business needs are translated into secure, efficient, and sustainable software solutions.

The Senior Director will partner closely with R&D, clinical laboratory, quality, operations, product, and executive stakeholders to define technical roadmaps, manage delivery commitments, and ensure production systems meet the reliability, scalability, traceability, and compliance needs of a genomic diagnostics organization. This includes overseeing the transition of research methods and analytical prototypes into validated production workflows, modernizing legacy systems, strengthening software development lifecycle practices, and improving automation, documentation, and release management across the team.

Success in this role requires a seasoned engineering leader with strong software architecture judgment, practical knowledge of bioinformatics and clinical genomics workflows, experience leading software teams in regulated or quality‑controlled environments, and the ability to balance strategic planning with hands‑on technical oversight. The ideal candidate will be a collaborative, accountable, and execution‑oriented leader who can mentor managers and engineers, promote engineering excellence, and deliver software capabilities that advance Baylor Genetics’ diagnostic services and long‑term technology strategy.

KEY RESPONSIBILITIES

• Software Engineering Strategy and Leadership:
  • Define and own the software engineering vision, technical roadmap, delivery priorities, and operating model for the Bioinformatics Software Engineering team in alignment with Baylor Genetics’ clinical, scientific, operational, and business objectives.
  • Lead, develop, and mentor a high‑performing team of software engineers, bioinformatics engineers, fostering a culture of accountability, collaboration, engineering excellence, and continuous improvement.
  • Establish team goals, resource plans, delivery commitments, performance expectations for critical software engineering capabilities.
  • Represent software engineering priorities, risks, dependencies, and tradeoffs to senior leadership and cross‑functional stakeholders.
• Clinical Genomics Platform Delivery and Production Operations:
  • Oversee the design, development, implementation, maintenance, and operational support of production‑grade software platforms, bioinformatics workflows, data systems, and reporting solutions that enable clinical genomic diagnostics.
  • Ensure production systems are scalable, reliable, secure, observable, validated, well‑documented, and maintainable in a clinical laboratory and quality‑controlled environment.
  • Drive modernization of legacy systems, workflow orchestration, automation, containerization, cloud or HPC‑based compute strategies, data integration, and software architecture to improve performance, reliability, and long‑term sustainability.
  • Establish operational support models, escalation processes, incident response practices, root‑cause analysis expectations, and post‑release monitoring for critical software and bioinformatics systems.
• Cross‑Functional Partnership and Delivery Execution:
  • Partner with R&D, clinical laboratory, quality, operations, product, data, and executive stakeholders to translate scientific innovation, clinical needs, and business priorities into actionable software roadmaps and delivery plans.
  • Lead prioritization, scope definition, dependency management, timeline planning, and delivery governance for software initiatives that support new test launches, assay improvements, workflow enhancements, and operational efficiencies.
  • Guide the transition of research algorithms, analytical prototypes, scripts, and proof‑of‑concept tools into validated, production‑ready software components and workflows.
  • Communicate progress, risks, decisions, and tradeoffs clearly to technical and non‑technical audiences, including executive stakeholders.
• Software Quality, Compliance, and Release Governance:
  • Define and enforce software development lifecycle standards, including requirements management, architecture review, code review, version control, automated testing, validation support, documentation, deployment readiness, and release management.
  • Ensure software engineering practices support applicable quality, regulatory, privacy, security, traceability, and audit‑readiness expectations for clinical genomics and diagnostic testing environments.
  • Establish metrics and governance mechanisms to monitor software quality, delivery performance, system reliability, production incidents, technical debt, and continuous improvement opportunities.
  • Promote engineering practices that improve reproducibility, maintainability, automation, observability, and long‑term operational resilience.
• AI‑Enabled Engineering and Workflow Innovation:
  • Lead responsible exploration and implementation of AI‑enabled software engineering, workflow automation, decision‑support, documentation, code assistance, issue triage, and operational support capabilities where they improve quality, efficiency, and scalability.
  • Establish appropriate governance, human oversight, privacy safeguards, traceability, and reliability standards for AI‑assisted tools used in software development, bioinformatics workflows, and operational processes.
  • Promote adoption of AI‑enabled practices that improve engineering productivity while maintaining clinical‑grade quality, compliance, and accountability.

QUALIFICATIONS

Required

• • Bachelor’s degree in Computer Science, Software Engineering, Bioinformatics, Computational Biology, Genomics, Data Science, or a related quantitative or technical field.
• • 12+ years of experience in software engineering, bioinformatics software development, clinical genomics technology, data platform development, or related computational technology roles.
• • 7+ years of experience leading software engineering teams, including direct people management, performance management, mentoring, hiring, organizational planning, and management of technical leads or managers.
• • Proven experience defining software engineering strategy, technical roadmaps, delivery priorities, resource plans, and operating models for complex software teams or platforms.
• • Demonstrated experience leading development and support of production‑grade software systems, bioinformatics workflows, data platforms, workflow automation, or reporting applications used in clinical, healthcare, laboratory, or regulated environments.
• • Strong knowledge of software architecture, system design, API design, data integration, database‑backed applications, workflow orchestration, scalable compute, and modern software development lifecycle practices.
• • Experience establishing or improving engineering standards for requirements management, code review, automated testing, CI/CD, validation support, documentation, release management, incident management, and production support.
• • Practical understanding of NGS data, clinical genomics workflows, bioinformatics pipelines, genomic data formats, variant analysis workflows, laboratory interfaces, or diagnostic reporting systems.
• • Experience translating research prototypes, analytical methods, scripts, or proof‑of‑concept tools into scalable, tested, validated, documented, and maintainable production workflows.
• • Experience working with quality, regulatory, privacy, security, traceability, and audit‑readiness expectations in clinical diagnostics, healthcare technology, laboratory operations, or other regulated software environments.
• • Strong technical fluency in software engineering tools and practices, including Git‑based development, code review, automated testing, CI/CD, Linux environments, containerization, workflow orchestration, cloud or HPC compute, databases, APIs, and monitoring or observability tools.
• • Ability to communicate complex technical concepts, delivery risks, architectural tradeoffs, and operational priorities clearly to executive, technical, scientific, clinical, quality, and business stakeholders.

Preferred:

• • Master’s degree or PhD in Computer Science, Software Engineering, Bioinformatics, Computational Biology, Genomics, Data Science, or a related field.
• • 12+ years of experience leading or managing software engineering, bioinformatics engineering, platform engineering, or data engineering teams in healthcare, diagnostics, life sciences, or regulated technology environments.
• • Experience leading managers, technical leads, or multi‑functional engineering teams across multiple platforms, programs, or product areas.
• • Experience modernizing legacy systems, improving software architecture, reducing technical debt, and implementing scalable platform or workflow automation strategies.
• • Experience with Nextflow, Docker, Kubernetes, cloud platforms, HPC environments, microservices, API‑driven architectures, data lakes, data warehouses, or enterprise data integration patterns.
• • Experience applying AI‑enabled engineering tools, large language models, agentic AI, automation, or decision‑support technologies to improve software development productivity, workflow automation, documentation, issue triage, or operational support.
• • Experience supporting CAP/CLIA‑regulated laboratory environments, molecular diagnostics, clinical genomics, variant interpretation, reporting workflows, or healthcare data interoperability.

KEY PERFORMANCE INDICATORS

• • Strategic Roadmap and Delivery Execution:
  • o Achievement of annual and quarterly software engineering roadmap milestones aligned with clinical, operational, R&D, and business priorities.
  • o On‑time delivery of committed software initiatives, platform enhancements, workflow improvements, and new test launch support within agreed scope and quality expectations.
  • o Effective prioritization and management of cross‑functional dependencies, delivery risks, resource constraints, and executive‑level tradeoff decisions.
• • Production Reliability and Operational Excellence:
  • o Improvement in uptime, system availability, workflow reliability, incident response time, and mean time to resolution for critical clinical genomics software and bioinformatics systems.
  • o Reduction in recurring production issues, manual interventions, workflow bottlenecks, and preventable operational escalations.
  • o Implementation of effective monitoring, observability, root‑cause analysis, escalation, and post‑release support practices for production platforms.
• • Software Quality, Compliance, and Release Governance:
  • o Adherence to software development lifecycle standards, including requirements documentation, code review, automated testing, validation support, release readiness, and change control expectations.
  • o Improvement in software quality metrics such as defect rates, test coverage, release stability, documentation completeness, and audit‑readiness of software deliverables.
  • o Successful support of quality, regulatory, security, privacy, and traceability requirements for clinical diagnostics and laboratory software systems.
• • Team Leadership and Organizational Health:
  • o Development, retention, engagement, and performance of software engineering talent, technical leads, managers, and critical subject matter experts.
  • o Progress against hiring, succession planning, career development, mentoring, and team capability‑building objectives.
  • o Improvement in team execution practices, accountability, collaboration, engineering standards, and cross‑training across key platforms and workflows.
• • Architecture Modernization and Technical Debt Reduction:
  • o Progress in modernizing legacy applications, reducing technical debt, standardizing platforms, improving documentation, and implementing sustainable architecture patterns.
  • o Measurable improvements in workflow automation, scalability, maintainability, performance, deployment efficiency, and system integration across bioinformatics and clinical software platforms.
  • o Effective governance of technology decisions, build‑versus‑buy evaluations, platform investments, and long‑term architecture alignment.
• • Stakeholder Partnership and Business Impact:
  • o Stakeholder satisfaction with software engineering responsiveness, communication, transparency, delivery quality, and partnership with R&D, clinical laboratory, quality, operations, product, and executive teams.
  • o Demonstrated contribution of software engineering initiatives to improved diagnostic workflow efficiency, turnaround time, scalability, data quality, automation, and operational productivity.
  • o Clear and timely communication of roadmap status, risks, dependencies, technical tradeoffs, and recommended decisions to senior leadership.
• • Innovation and AI‑Enabled Productivity:
  • o Responsible adoption of AI‑enabled engineering, automation, documentation, triage, workflow support, or productivity tools where they provide measurable improvements in quality, efficiency, and scalability.
  • o Implementation of appropriate governance, human oversight, privacy safeguards, and reliability standards for AI‑assisted engineering and operational capabilities.
  • o Measurable improvement in engineering productivity, knowledge management, documentation quality, or issue resolution efficiency through approved automation and AI‑enabled practices.

COMPETENCIES

• • Strong software engineering judgment, analytical thinking, and practical problem‑solving skills.
• • Ability to translate scientific requirements and R&D methods into reliable, maintainable, production‑ready software and workflows.
• • Ability to collaborate effectively with scientists, software developers, clinical reviewers, laboratory personnel, quality teams, and business stakeholders.
• • Excellent written and verbal communication skills.
• • Commitment to quality, reproducibility, documentation, automation, maintainability, and operational excellence in a clinical genomics environment.

PHYSICAL DEMANDS AND WORK ENVIRONMENT

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

EEO STATEMENT

Baylor Genetics is proud to be an equal opportunity employer committed to fostering an inclusive and diverse workplace. We welcome and encourage applicants from all backgrounds to apply. We do not discriminate on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, age, veteran status, disability, genetic information, pregnancy, childbirth, or any other status protected by applicable federal, state, or local law. If you need an accommodation during the application process, please contact our Human Resources team.

Equal Opportunity Employer
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