Jobs / United States / Humana INC
Director - Enterprise Quality Methologies, Governance & Enablement
Humana INC · 🇺🇸 New York, NY
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 130 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Humana INC paid sponsored hires in similar roles1 certified filing for “Director, Data Engineering” (Computer and Information Systems Managers) in NY: $261k–$261k, median $261k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Humana INC
The US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 130 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Humana INC →
About the role
Become a part of our caring community Humana is committed to helping people achieve their best health by delivering innovative technology solutions that power exceptional member, provider, and associate experiences. The Director, Enterprise Quality Methodologies, Governance & Enablement will lead the definition, governance, and enterprise adoption of quality engineering standards, methodologies, compliance alignment, quality intelligence, AI-enabled engineering practices, and transformation enablement. This leader will establish “what good looks like” for Quality Engineering across Humana’s software delivery ecosystem and ensure quality practices are consistently embedded across the SDLC, Agile ways of working, engineering platforms, DevOps governance routines, and responsible use of AI-enabled capabilities. This role will partner closely with Quality Engineering, Enterprise Engineering Enablement, Agile Office, Product, Engineering, Architecture, Cybersecurity, Risk, Compliance, Audit, Data and AI teams, and segment technology teams to mature enterprise quality practices, strengthen governance frameworks, enable platform adoption, and improve quality outcomes across the organization. Key Responsibilities Enterprise Quality Strategy, Policy, and Standards • Define and evolve the enterprise Quality Engineering strategy, standards, methodologies, and operating procedures across software delivery teams. • Establish SDLC quality standards, policies, and practical guidance that embed quality earlier and more consistently across planning, development, testing, release, and production validation. • Create and maintain enterprise engineering best practices, including “What Good Looks Like” guidance for quality planning, test automation, code quality, regression testing, deployment validation, performance testing, release readiness, production quality, and AI-assisted quality engineering practices. • Align Quality Engineering ways of working with Agile Office practices, DevOps governance, platform standards, enterprise delivery models, and responsible AI expectations. • Partner with engineering enablement teams to ensure quality standards are reflected in enterprise tooling, pipelines, templates, reusable workflows, development practices, and AI-enabled engineering workflows where appropriate. AI-Enabled Quality Engineering and Transformation • Lead the integration of AI-enabled quality engineering practices into enterprise methodologies, governance models, coaching programs, and SDLC quality standards. • Define guidance for responsible use of AI in quality engineering, including test design, test generation, defect analysis, requirements analysis, code review support, test data analysis, automation acceleration, quality intelligence, and productivity improvement. • Partner with Data, AI, Cybersecurity, Risk, Compliance, Legal, and Engineering stakeholders to ensure AI-enabled quality practices align with enterprise policies, risk controls, privacy requirements, and responsible AI principles. • Evaluate opportunities to use generative AI, machine learning, analytics, and intelligent automation to improve quality planning, coverage analysis, risk-based testing, defect prevention, release readiness, and production quality insights. • Establish governance and measurement practices for AI-enabled quality capabilities, including adoption, value realization, risk management, human oversight, and continuous improvement. Governance, Compliance, and Quality Maturity • Establish and maintain enterprise Quality Engineering governance frameworks that improve consistency, transparency, risk management, compliance alignment, and responsible AI adoption. • Define, implement, and mature a Quality Engine