Sr. Data Engineer
$180k - $210kPlug
Job Title: Sr. Data Engineer Team: Engineering Location: Onsite - Sawtelle (Between Santa Monica & West LA) Employment Type: Full-time, Salaried, Exempt Reports To: Head of Engineering About Plug Plug is an automotive fintech startup building the marketplace for electric vehicles. Consumers, dealers, and commercial partners trade in, sell, and auction used EVs through a platform built exclusively for electric, with nothing adapted from legacy automotive. Consumers can get an instant cash offer in minutes, backed by data-driven pricing, battery state-of-health analysis, with pickup, payment, and title transfer handled end-to-end. Franchise and independent dealers across the United States bid in real time on inventory sourced directly from these consumers, dealers, and commercial partners. The result is a single platform that gives sellers a fast, fair exit and gives dealers a dedicated channel for sourcing used EV inventory. Founded in 2023 and led by former Tesla executives, Plug has facilitated $100M in EV sales since launch and is backed by a $20M Series A from Lightspeed Venture Partners. We're building the most AI-native startup team in Los Angeles, full of operators, engineers, and ML researchers laying the transaction rails for the used EV market's exponential growth. Why Plug
- You'll own a piece of the biggest infrastructure opportunity in automotive.
- You'll work on a platform built exclusively for EVs, with nothing adapted from legacy automotive.
- You'll be part of an AI-native company. We're rethinking how work gets done at a fundamental level.
- You'll work directly with operators who have scaled multi-billion dollar businesses in automotive, EV, and marketplaces.
- You'll have real ownership, high autonomy, and a direct line to company outcomes.
- Own the dbt transformation layer - staging, intermediate, and mart models and MetricFlow definitions in the Semantic Layer - so Plug's business data is queryable by both humans and AI agents with the right business context.
- Build and maintain the ingestion layer: Fivetran pipelines and HubSpot integration for existing sources, and onboarding new ones - OEM partner APIs, dealer marketing feeds, auction signals, and attribution data - with monitoring and change control.
- Work with the dbt MCP server and Semantic Layer as Plug's AI agent data interface - no prior MCP experience required, but genuine excitement about the direction where agents replace dashboards as the primary way data is consumed.
- Maintain the ML feature store alongside our ML and data engineer: training data quality, schema stability, and documentation of the data foundation Rain Radar and future models depend on.
- Support internal data consumers by surfacing trusted datasets, enabling self-service reporting, and occasionally building reports to meet business needs.
- Collaborate with the engineering team and ML and data engineer; be a strong voice in data architecture decisions and schema design reviews.
- Use agentic coding tools to write clear, tested, and maintainable transformation logic; bring a "capture everything" instinct to data collection decisions - think proactively about what should be captured, not just what is already specified.
- Own and contribute to data quality monitoring, alerting, and remediation - the data trust standard the whole organization relies on.
- Own production data pipelines, participate in incident response, and debug data issues across the stack.
- Actively raise the data engineering bar through thoughtful code review, documentation, and knowledge sharing - including how you use AI tools.
- 5+ years building production dbt projects end-to-end: staging through mart layers, test coverage, schema change management, and ideally MetricFlow or semantic layer work.
- Genuine curiosity about AI-native data patterns - excited about the dbt Semantic Layer as an agent interface and building data infrastructure that AI agents query rather than humans navigating dashboards. No experience required; curiosity is.
- Cross-context ML awareness - has worked near an ML team and understands how upstream data quality decisions affect model training and feature quality, not just analytics accuracy.
- Snowflake or major cloud warehouse experience - schema design, role-based access control, and performance optimization.
- Fivetran or comparable ELT tool experience - managing pipelines and onboarding new sources with reliability and observability.
- Advanced SQL and Python for transformation logic, data quality checks, and pipeline automation.
- Experience integrating REST APIs and third-party data sources; comfortable with reliability patterns.
- Operates in agentic development workflows with a high quality bar for AI-generated output.
- Prior startup or small-team experience with a track record of owning data infrastructure end-to-end. Authorized to work in the US for any employer.
- Health, Vision, and Dental insurance
- 401(k) plan
- Daily lunch stipend
- Equity (based on role and performance)
- Unlimited PTO
- Fully stocked kitchen
- Dog-friendly office
- Ongoing opportunities for growth and development
Vacancy posted 3 days ago
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