Senior Analytics Engineer

Vay•Las Vegas, NV

About The Position

Ever imagined saying, “I helped launch the future of transportation”? We’re rewriting the rules of urban mobility. At Vay, customers tap a button and a car arrives - with no one inside - powered by our world-first Remote Driving technology on real public streets. We’re live in Las Vegas and scaling fast, powered by a strategic investment of up to $410 million from Grab. Our mission is simple: replace private car ownership with a faster, cleaner, door-to-door mobility model. If you want to build something real, visible, and genuinely transformative, you’ll feel right at home here. What’s happening at Vay: First to roll out vehicles in both Europe and the USA on public roads without a human being inside & one of only two companies in the States operating a commercial driverless service. We've partnered with Kodiak to bring remote-driving technology to autonomous trucks in the Bay Area, showcasing our ability to integrate our system into heavy-duty commercial vehicles and expand the applications of our tech across the US. We’ve completed 10’s of thousands of trips so far and are busy multiplying our Las Vegas fleet just to meet demand! ...And soon we’ll be expanding across the US & Europe 🚀 Why this role is exciting: This role sits at the core of our Operations team in Las Vegas, where data drives day-to-day decisions. You’ll work directly with onsite operational leaders to identify opportunities, solve real-world problems, and build analytics products that materially improve performance, efficiency, and customer experience. This is a high-impact, hybrid analytics engineering / business intelligence role. You won’t be buried in dashboards no one uses. You’ll shape how our customer-facing services operate and scale, with a direct line between your work and operational outcomes. If you want ownership, visibility, and impact, this role delivers all three.

Requirements

Strong end-to-end analytics ownership - you’ve taken data products from concept through to production.
Proven track record of impact - your work has directly improved business or operational outcomes.
Expert-level SQL skills - complex, performant queries are second nature.
Strong experience with dbt, including designing sustainable, reusable data models.
Proficiency with BI tools such as Superset, Looker, or similar, and a sharp eye for clear, actionable dashboards.
Comfortable working autonomously
Experience with cloud data warehouses (e.g. Snowflake, BigQuery).
Familiarity with modern data tooling, including:
ETL / reverse ETL tools (Airbyte, Hightouch)
Data flow / integration tools (NiFi, OpenFlow)
Comfortable handling light engineering work, such as:
Working with APIs
Configuring visualization tools
Setting up integrations
Solid understanding of time-series data and operational metrics, ideally with exposure to observability or real-time monitoring.

Nice To Haves

Experience in fast-paced environments such as startups, scale-ups, or operations-heavy businesses.
Familiarity with scripting (Python, JavaScript, etc.) for automation or lightweight tooling.
Exposure to operational domains like mobility, logistics, fleet, or marketplaces.
A strong product mindset - you think in terms of users, feedback loops, and delivered value, not just queries and pipelines.

Responsibilities

Own end-to-end analytics solutions, from data modeling (SQL, dbt) through to dashboards and insight delivery (Superset, Grafana, Dekart).
Act as the primary data partner for Operations and Mobility Product, working closely with Ops and Product leaders to:
Identify opportunities
Define and refine KPIs
Drive continuous performance improvement
Deliver measurable impact across critical operational areas including:
Fleet readiness
Maintenance performance
Charging and vehicle positioning optimization
Overall operational efficiency
Design and maintain self-serve dashboards, alerts, and reporting interfaces that give Ops teams real-time visibility and decision-making power.
Translate business and operational questions into actionable, data-driven solutions using strong domain context and product thinking.
Collaborate with Data Science teams to align on data practices, tooling, and standards across Vay.
Move fast, iterate often, and take ownership — balancing pragmatic delivery with building scalable, long-term data foundations.