Sr. Data Engineer

Plug Technologies Inc•Los Angeles, CA

28d•$180,000 - $210,000•Onsite

About The Position

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.

Requirements

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.

Responsibilities

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.