Sr. Product Manager, Test Management Platform

Engineering Workflow Platforms builds and operates the software Lucid uses to run its vehicle programs. Five products are live in production today, covering the planning bill of materials, engineering change management, software feature ownership, release readiness, and test fleet and vehicle build management. They are not five separate tools. Each one is a window onto a single data model that reconciles six systems of record, including Dassault 3DEXPERIENCE, SAP, MES, Auros, Jira, and Salesforce into one trustworthy answer to whether a program is ready for its next gate.

We are forward deployed. We sit directly with Program Management, Engineering, Supply Chain, and Supplier Quality. We set our own OKRs and report against them in Monthly Business Reviews. AI-assisted work is core to how we operate, and it is what allows a team this size to carry this much surface area. The team is small, and it is growing.

Role:

We are seeking a Senior Product Manager to own verification and test management across the platform. That is the chain that runs from a requirement, to the test cases derived from it, to the runs that execute those cases, to the result a gate review accepts as evidence.

The scope covers hardware and software. On the hardware side the platform has to carry the DVP, test requests, sample and build allocation, lab and track capacity, and the DV and PV campaigns behind each program gate. On the software side it has to carry suites running in CI, on bench, on HIL, and in vehicle, tied to a release candidate and to the feature they verify.

The role is not the DVP owner and does not run tests. Test and Validation engineering does both, and will continue to. This will own the tooling they work in, the data model underneath it, and whether the coverage and evidence that come out of it survive a gate review. This is an internal product role, not a validation engineering role.

This role is based in Phoenix, AZ and requires employees to be onsite five days a week.

Key Responsibilities:

• Capability Ownership: Verification and Test Management
  • Own the strategy and roadmap for test case management and test run management as one connected capability, and carry that area into the platform roadmap.
  • Own the traceability model that links requirement to test case to test run to result to defect, and hold it steady as new programs, new variants, and new test types arrive
  • Sequence delivery against real vehicle program gates, and be able to state which gate a given scope or staffing decision causes us to make or miss
  • Define the coverage and readiness measures a gate review actually uses
  • Own source-of-truth decisions inside your area across systems and define the reconciliation rules
  • Protect the integrity of the model against expedient one-off requests, and be able to explain the refusal in terms the requester accepts
  • Work through with the manager what belongs in the data model, in a pipeline, in the application, and in a process outside the system entirely
• Requirements and Traceability
  • Make coverage answerable on demand: which requirements have test cases, which cases have run, which runs passed, and what is left before the gate
  • Drive migration off legacy requirements and test tooling, including the data cleanup and the working habits that migrate with the data
  • Keep the verification evidence an audit or a safety case requires attached to the object that produced it
• Test Case and Test Run Management
  • Own the architecture of the test case library: authoring, review, reuse across variants and programs, versioning, and the link back to the requirement each case verifies
  • Own the paradigm for traceability of execution: campaigns and plans, scheduling against labs, benches, rigs, tracks, and test fleet vehicles, results capture, retest, deviations and waivers, and defect linkage
  • Make hardware and software execution reconcile into a single program-level verification picture rather than two parallel ones
  • Partner with test engineering on where the platform should automate and where it should stay out of the way
• Cross-Functional Leadership
  • Build and hold relationships with director-level stakeholders across Test and Validation, Systems Engineering, Software, Hardware Engineering, Quality, Homologation, and Program Management
  • Take recommendations into VP-level forums with the manager, and be the person who answers the detail questions in the room
  • Broker priority conflicts between functions with legitimate competing needs, and get to a decision that holds
  • Represent the capability in program reviews and gate reviews so the roadmap stays tied to what programs actually need next
• Discovery, Definition, and Decisions
  • Run discovery with the people who actually run tests: in the lab, at the bench, on the track, and in the pipeline, not only in the review meeting
  • Write the specifications, decision records, and proposals that set direction, covering the problem, the options considered, the trade-offs, the blast radius on live production data, and the rollout and rollback plan
  • Hold a high bar on definition quality, and review peer work against it
• Delivery and Launch
  • Own launches that carry real organizational risk: pilot selection, readiness criteria, phased rollout, and a credible fallback if adoption stalls
  • Generate Training and Communications on the enablement and change management that determines whether a launch actually lands
  • Measure adoption and data quality after launch, and keep working the gap instead of declaring done

Qualifications:

• Product management experience with end-to-end ownership of an enterprise, internal platform, or business systems product, with a track record you can walk through
• Direct experience with verification or test management as a product or process domain: requirements management, test case management, and test run management.
• A working understanding of how hardware verification and software verification differ, and of what the two have to share for a program-level answer to exist at all
• Demonstrated ownership of a system of record rather than a reporting layer, including responsibility for data correctness and for the downstream consequences of getting it wrong
• Proven ability to drive change
• Strong systems and data thinking: sources of truth, reconciliation logic, data modeling and taxonomy design, integration latency, and failure behavior when systems disagree
• Excellent written communication: specifications, decision records, and proposals that are read and acted on without you presenting them
• Credibility with engineers: you can sit with a test engineer, follow the procedure, and leave with a specification
• Data literacy sufficient to work independently: Jira and JQL, working SQL or equivalent, and the instinct to interrogate a number before repeating it
• Effective use of GitHub Copilot or comparable AI assistants across research, analysis, prototyping, and drafting, with the judgment to verify output rather

Preferred:

• Automotive, aerospace, medical device, or hardware NPI domain depth: DVP, DV and PV campaigns, phase gates, launch quality, or program milestone governance
• Requirements management at automotive scale, including traceability across product, system, component, and functional safety levels, and understanding of legacy tool such as Jama, DOORS, Polarion, or Codebeamer
• Test management tooling experience: Xray, Zephyr, TestRail, Polarion, Auros, or an equivalent, including the coverage and reporting layer above it
• Familiarity with automotive test infrastructure
• Functional safety exposure, including ISO 26262 and ASIL decomposition
• Automotive SPICE familiarity, particularly the system and software verification processes and what an assessment expects to see
• Phase gate or stage gate product development process governance, and the executive reporting layer above it
• PLM experience, particularly Dassault 3DEXPERIENCE and ENOVIA, or Siemens Teamcenter or an equivalent
• Depth in the Atlassian ecosystem: Jira architecture at scale, Jira Assets as a master data model, and applications built on Atlassian Forge
• Experience building AI-assisted workflows into a product practice, including standards for how AI-generated analysis and specifications are reviewed