Software Developer
$134.7k - $185.24kFull-time
Lucid Motors
About Lucid At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing™” approach to the global automotive market. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience. We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility. Leading the future in luxury electric and mobility At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility. We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience. Come work alongside some of the most accomplished minds in the industry. We're providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you. About The Team Engineering Workflow Platforms is a small, forward-deployed team that builds and operates the systems Lucid uses to run its vehicle programs. Engineering release data lives across disconnected systems: 3DX / R-Tree, Amazon Redshift, Moku, Pegasus, Auros, Jira, and Jira Assets. We are business systems architects and developers embedded directly with the business rather than a ticket queue behind it. We own production Python pipelines, a multi-program executive analytics suite, a growing portfolio of Atlassian Forge applications used daily across the engineering organization, and the Jira and Assets architecture underneath them. AI-assisted development is a core part of how we work, and it is what allows a team this size to carry this much surface area. Role We are seeking a Software Developer to own and extend that estate: keeping it correct, keeping it running, and scaling it as new vehicle programs come online. The work spans production data integration, executive-grade data visualization, and full-stack development on a live Jira platform application that program teams depend on every day. This is not a ticket-taking role. You will decide which system wins for a given field, where a piece of logic belongs, how fresh the data must be, and what the failure behavior should be. You will write those decisions down, socialize them, and defend them. The ideal candidate pairs strong Python and data engineering fundamentals with the judgment to change business-critical production records safely and reversibly. This role is based in Newark, CA and requires employees to be onsite five days a week. Responsibilities
- Data Reconciliation Pipelines (3DX / Redshift to Jira)
- Build and operate multi-program Python pipelines that detect discrepancies between the engineering system of record and Jira, then correct them at scale across release status, electrical impact, ECR links, Assets object links, and 3DX variant codes
- Maintain a single shared classification engine so no two pipeline stages can disagree on which source row drives a decision
- Hold the line on safety semantics: dry-run by default, batched execution, revert snapshots for every write, and idempotent re-runs, at a scale of thousands of Jira field writes per production run
- Synchronize Launch Quality Plan stage dates (S1 through S4) from Auros and Pegasus, and upsert thousands of Engineering Change Request records from Redshift into Jira Assets with change detection and per-run diff audit trails
- Executive Analytics and Dashboards
- Extend a shared Python rendering library and per-program dashboard suites covering OKTC tooling kick-off status, plan versus actual release curves, LQP / PPAP maturity, MRD shipment risk, PV readiness, and ECR cycle time
- Build interactive views for executive audiences: milestone tiles, date-range brushing, click-to-drill filtering, JQL copy, and CSV export
- Keep the suite configuration driven (YAML for Jira base, custom field IDs, buckets, taxonomies, per-program milestones and JQL) so new programs and milestones are added without forking code
- Operate the orchestration layer: parallel execution behind a run lock, pinned run timestamps, schema pre-checks, shared fetch caching, and retry on transient failures
- Production Jira Platform Development (Atlassian Forge)
- Develop and release changes to a live Jira global-page app and issue panels, spanning roughly forty backend resolvers over Jira and Jira Assets covering revisions, base parts, stage gates, MRD / GSM / VPPR views, Part Passport workflows, bulk updates, and gate rescheduling
- Build front-end interactions in vanilla JavaScript Custom UI: sortable and filterable data grids with inline editing, spreadsheet-style bulk fill with multi-select copy, paste and drag, an interactive Gantt with drag-rescheduling and dependency-aware gap preservation, and SVG chart views
- Own event-driven behavior: issue-update triggers that reconcile shipment gate sets, web triggers for index hygiene, and scheduled triggers that warm the cache layer
- Enforce fail-closed access control and deploy guardrails, where instance profiles are the only source of Jira URLs and custom-field IDs
- Architecture, Proposals, and Technical Decision-Making
- Make and defend architectural decisions, including choosing the source of truth for a given field, deciding what belongs in a pipeline versus a dashboard versus the platform app, and setting caching and freshness contracts
- Write technical proposals for new capabilities and significant changes covering problem statement, options considered, trade-offs, blast radius on live production data, and the rollout and rollback plan, then socialize them with engineering and business stakeholders before building
- Production Operations and Release Safety
- Own production readiness end to end: linting, static typing, test coverage, CI, guardrail validation, staged rollout, live smoke testing, and a documented rollback path for every release
- Work day to day in GitLab with feature branches, merge requests under review, CI pipelines as the merge gate, and issue boards for planning and traceability, so every production release traces back to a merged MR
- Use GitHub Copilot and agent-based coding tools to accelerate scaffolding, test generation, refactoring, and exploration, while independently verifying every generated change against tests, linting, and live data
- Investigation, Collaboration, and Knowledge Transfer
- Root-cause cross-system data discrepancies across 3DX, Redshift, Jira, and Assets, and publish written, evidence-backed findings that other teams can act on
- Partner directly with Program Management, Tooling, Supplier Quality, and TPM stakeholders to translate launch-readiness questions into data products
- Maintain architecture documentation, developer guides, changelogs, and deployment runbooks alongside the code
- Production software engineering experience with meaningful ownership of data integration or internal platform tooling
- Strong Python: package structure, type annotations, testing, CLI design, and concurrency
- Practical SQL against large, messy operational warehouse tables (Amazon Redshift or equivalent)
- Hands-on Jira REST API and Jira Assets / AQL experience, including failure modes, pagination and row limits, custom-field payload shapes, and write semantics, not just the happy path
- Modern JavaScript without a framework: ES modules, DOM APIs, SVG rendering, and event delegation
- Disciplined engineering habits: unit and characterization tests, linting, static typing, coverage gates, and CI
- Proficiency with GitLab or equivalent: branching strategy, merge requests and code review, CI/CD pipelines, and issue tracking
- Effective use of GitHub Copilot or comparable AI coding assistants, with the judgment to review, test, and reject generated code rather than trusting it
- Demonstrated ability to change live production systems safely, with dry runs, staged rollouts, and reversibility as the default posture
- Architectural judgment: able to weigh options, choose a source of truth, and explain the trade-off in writing
- Clear technical writing; able to author a proposal or design document and document a system well enough that someone else can operate it
- Atlassian Forge development: Custom UI, resolvers, KVS, scheduled and event triggers, and the deployment lifecycle
- Manufacturing, PLM, or NPI domain exposure: BOM structures, ECRs, stage gates, MRD, and PPAP or an equivalent supplier-quality maturity model
- Experience shipping to production environments without a full sandbox, designing changes that are independently verifiable and reversible
- Data visualization and dashboard design for executive audiences
- Experience hardening large, undertested legacy modules behind characterization tests rather than rewriting them
- Experience authoring architecture decision records (ADRs) and driving them to a decision with stakeholders
- Comfort building AI-assisted workflows into a team's development process, including prompt patterns, generated-code review standards, and agent tooling
Vacancy posted 3 days ago
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