Lucid Motors
Software Developer
About this role
Lucid seeks a Software Developer to own and scale the Engineering Workflow Platforms team's business-critical systems that integrate vehicle program data across disconnected enterprise tools. You'll build production Python pipelines, maintain data reconciliation workflows, and develop full-stack Jira applications while making architectural decisions that impact engineering teams daily.
What you'll do
- Design and operate multi-program Python pipelines that reconcile data between 3DX, Redshift, and Jira at scale
- Maintain shared classification engines and ensure consistency across data pipeline stages
- Implement safe production data updates with dry-runs, batching, snapshots, and idempotency for thousands of Jira field writes
- Build and extend executive-grade data visualization and analytics dashboards
- Develop full-stack Atlassian Forge applications used daily across the engineering organization
- Make architectural decisions on system integration, data freshness requirements, and failure behavior
What they're looking for
- Python (production-level proficiency)
- Data engineering and ETL pipeline design
- SQL and data warehousing (Redshift experience preferred)
- Jira platform development and Atlassian Forge
- Data reconciliation and integrity validation
- Systems integration and API design
- Ability to write and defend technical decisions
- Production incident response and debugging
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Lucid Motors
Lucid Motors manufactures electric vehicles and is hiring quality engineers to improve production processes and resolve customer issues at their Arizona facility. The company is looking for manufacturing and field quality professionals to drive process improvements, investigate warranty concerns, and enhance overall vehicle performance.
- Website
- lucidmotors.com
Likely interview questions
- Walk us through a time you designed a data pipeline that had to reconcile discrepancies between multiple source systems—what were your safety considerations?
- How would you approach making thousands of Jira field updates safely in production, and what guardrails would you implement?