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You will own the dbt transformation layer and ingestion pipelines to build a governed data foundation for AI-native applications. Additionally, you will collaborate with engineering and ML teams to maintain data quality, schema stability, and production data infrastructure.
Requirements
Candidates must have 5+ years of experience building production dbt projects and managing cloud data warehouses like Snowflake. Proficiency in Python, SQL, and experience with ELT tools like Fivetran is required, along with a strong interest in AI-native data patterns.
Full job description
Job Title: Sr. Data Engineer
Team: Engineering
Location: Onsite-Santa Monica, CA
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.
The Opportunity
We are looking for a Senior Data Engineer to build the governed data layer at the center of Plug's AI-native strategy. Plug runs on Snowflake, dbt, and Fivetran. The infrastructure is in place; the governed layer is still being built. You will own the dbt transformation models and certified metric definitions that the business runs on, the ingestion pipelines that bring in OEM partner feeds, dealer data, auction signals, and HubSpot, and the data foundation that Rain Radar - our production EV pricing model - trains on. You will work with the dbt Semantic Layer as an interface for AI agents to query Plug's data with business context. You report to our head of engineering and work closely with our ML and data engineer.
What You’ll Do
Data Platform
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.
Engineering
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.
What You’ll Bring
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.
Compensation and Benefits
Annual salary: $180,000 - $210,000
Salary ranges reflect independent, third-party benchmarking against national compensation data. Where an individual's actual offer falls within that range depends on a combination of factors, including relevant experience, specialized skills, training and qualifications, current business needs, and prevailing market conditions. Ranges are reviewed periodically and may be adjusted to reflect changing market conditions.
Benefits & Perks:
Health, Vision, and Dental insurance
Daily lunch stipend
Equity (based on role and performance)
Unlimited PTO
Fully stocked kitchen
Dog-friendly office
Ongoing opportunities for growth and development
This full-time position is based in Sawtelle (between Santa Monica and West Los Angeles) and is an in-person role. We welcome candidates from all locations to apply, provided they are willing to relocate for the role. Relocation assistance will not be provided for successful candidates. Sponsorship not available at this time.
Acknowledgments
Plug is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We believe diverse teams build better products - full stop.
Related keywords
Data EngineeringSnowflakedbtFivetranPythonSQLMetricFlowSemantic LayerMachine LearningFintechElectric VehiclesAPIELTData PipelineData QualityHubSpot
The future of the EV marketplace is right here. Buy and sell high-quality, used EVs though the marketplace built exclusively for electric vehicles. From first time sellers to large-scale dealers, Plug meets customers where they are in their EV journey.
How much do Engineering jobs in Los Angeles, CA pay?
Based on 2241 listings with disclosed salaries, most engineering jobs in Los Angeles, CA pay between $80k–$210k per year. Individual offers vary with seniority, company size, and specialization.
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