Software Developer

Software DeveloperNewark, CAAt 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 TeamEngineering 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 codesMaintain a single shared classification engine so no two pipeline stages can disagree on which source row drives a decisionHold 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 runSynchronize 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 trailsExecutive Analytics and DashboardsExtend 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 timeBuild interactive views for executive audiences: milestone tiles, date-range brushing, click-to-drill filtering, JQL copy, and CSV exportKeep 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 codeOperate the orchestration layer: parallel execution behind a run lock, pinned run timestamps, schema pre-checks, shared fetch caching, and retry on transient failuresProduction 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 reschedulingBuild 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 viewsOwn event-driven behavior: issue-update triggers that reconcile shipment gate sets, web triggers for index hygiene, and scheduled triggers that warm the cache layerEnforce fail-closed access control and deploy guardrails, where instance profiles are the only source of Jira URLs and custom-field IDsArchitecture, Proposals, and Technical Decision-MakingMake 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 contractsWrite 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 buildingProduction Operations and Release SafetyOwn 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 releaseWork 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 MRUse 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 dataInvestigation, Collaboration, and Knowledge TransferRoot-cause cross-system data discrepancies across 3DX, Redshift, Jira, and Assets, and publish written, evidence-backed findings that other teams can act onPartner directly with Program Management, Tooling, Supplier Quality, and TPM stakeholders to translate launch-readiness questions into data productsMaintain architecture documentation, developer guides, changelogs, and deployment runbooks alongside the codeQualifications:Production software engineering experience with meaningful ownership of data integration or internal platform toolingStrong Python: package structure, type annotations, testing, CLI design, and concurrencyPractical 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 pathModern JavaScript without a framework: ES modules, DOM APIs, SVG rendering, and event delegationDisciplined engineering habits: unit and characterization tests, linting, static typing, coverage gates, and CIProficiency with GitLab or equivalent: branching strategy, merge requests and code review, CI/CD pipelines, and issue trackingEffective use of GitHub Copilot or comparable AI coding assistants, with the judgment to review, test, and reject generated code rather than trusting itDemonstrated ability to change live production systems safely, with dry runs, staged rollouts, and reversibility as the default postureArchitectural judgment: able to weigh options, choose a source of truth, and explain the trade-off in writingClear technical writing; able to author a proposal or design document and document a