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Vay is a remote-driving mobility company operating the second-largest commercial driverless fleet in the Western World. Currently live in Las Vegas with strategic backing from Grab, the company is scaling rapidly and expanding across the US and Europe.
As Senior Analytics Engineer, you'll sit at the core of the Operations team, working 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 role combining analytics engineering and business intelligence—you'll own end-to-end analytics solutions from data modeling through to dashboards and insight delivery, not buried in unused dashboards.
Key responsibilities:
- Own end-to-end analytics solutions using SQL, dbt, Superset, Grafana, and Dekart
- Act as primary data partner for Operations and Mobility Product teams, identifying opportunities, defining KPIs, and driving continuous improvement
- Deliver measurable impact across fleet readiness, maintenance performance, charging/vehicle positioning optimization, and operational efficiency
- Design and maintain self-serve dashboards, alerts, and reporting interfaces for real-time Ops visibility
- Translate business and operational questions into actionable, data-driven solutions with strong domain context
- Collaborate with Data Science teams on data practices, tooling, and standards
- Move fast, iterate often, and balance pragmatic delivery with scalable data foundations
Requirements:
- Strong end-to-end analytics ownership: proven track record taking data products from concept to production
- Demonstrated impact: your work has directly improved business or operational outcomes
- Expert-level SQL skills with complex, performant queries as second nature
- Strong dbt experience, including designing sustainable, reusable data models
- Proficiency with BI tools (Superset, Looker, or similar) and sharp eye for clear, actionable dashboards
- Comfortable working autonomously
- Experience with cloud data warehouses (Snowflake, BigQuery)
- Familiarity with modern data tooling: ETL/reverse ETL tools (Airbyte, Hightouch), data flow/integration tools (NiFi, OpenFlow)
- Comfortable with light engineering work: APIs, visualization tool configuration, integrations
- Solid understanding of time-series data and operational metrics, ideally with observability or real-time monitoring exposure
Nice to have:
- Experience in fast-paced environments (startups, scale-ups, operations-heavy businesses)
- Scripting familiarity (Python, JavaScript) for automation
- Exposure to mobility, logistics, fleet, or marketplace operational domains
- Strong product mindset focused on users, feedback loops, and delivered value
