Software Developer

Lucid Motors•Newark, CA

19d•$134,700 - $185,240•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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
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
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
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