About ev.energy
We are building the world's largest EV-centric virtual power plant (VPP). By connecting to vehicles, chargers, batteries, solar, and other distributed energy resources, we turn households into flexible grid assets that balance supply and demand, preventing blackouts and reducing reliance on fossil fuels.
We are scaling rapidly across North America and Europe. We are moving fast, we are AI-first, and we are looking for builders who want to power the grid of the future.

About the role
Data is at the heart of everything ev.energy does — from measuring charging events and grid impact to calculating the utility incentives we pay drivers. As we scale our data function, we're hiring a Senior Data Engineer to lead the design of our warehousing architecture and own our pipelines end-to-end, from ingestion through to the analytics and payouts they power.

• 5+ years in data engineering (or backend engineering with a heavy data focus)
• Hands-on at scale with a cloud warehouse (Snowflake ideally) and dbt; strong Python and SQL; solid data modeling, warehousing, and schema design
• Comfortable owning systems end-to-end in a fast-moving environment — you build it, you run it
• Already using AI coding tools day-to-day and excited to push how far they go

Also nice to have

• Experience supporting production ML or real-time analytics workflows
• Familiarity with CDC / streaming (Apache Iceberg, S3) and BI tooling (Holistics, Hex)
• Background in energy, IoT, payments, or another high-volume, correctness-critical domain

• Design, build, and own scalable data pipelines across our modern data stack (Fivetran, Snowflake, dbt, with Holistics and Hex for analytics), from ingestion to warehousing to delivery
• Lead architectural decisions across the data platform — including infrastructure like our OLake → Iceberg → Snowflake pipeline — setting technical direction and establishing performance and cost baselines
• Own data quality, lineage, and observability: freshness, completeness, schema-drift detection, cost-per-job, and SLAs — the correctness our incentive payouts depend on
• Partner with engineering and analysts on projects like telematics accuracy and the driver incentive reporting, turning business requirements into reliable, well-modeled data
• Model data for both operational and analytical use cases, and build the dbt transformation layers (staging / intermediate / mart) that keep it maintainable and transparent

AI-first development

• Use AI tooling (Claude, Cursor, and equivalents) as a core daily practice — to build pipelines, write and review SQL and dbt models, and move faster without cutting corners on quality
• Our team runs on AI-native infrastructure (agentic routines, shared skills, an internal competency framework); you'll use it, improve it, and bring the best of the ecosystem back to the team