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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** VAY LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Business Analytics Applications, BigQuery, Cloud Database, System Configuration, Data Governance, Extract Transform Load (ETL), Data Visualization, Python (Programming Language), OpenFlow, Standard Sql, SQL Databases, Data Streaming, Scripting, Snowflake, Grafana, Data Analytics, Integration Frameworks, Apache Nifi, Api Design, Looker Analytics - **Published:** September 29, 2026 - **Apply:** https://startup.jobs/senior-analytics-engineer-vay-10205904 ## About the Role * 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 have * 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. ## Description * We've partnered with Kodiak to bring remote-driving technology to autonomous trucks in the US, 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 As a Senior Analytics Engineer, you'l be sitting at the core of our Operations team, 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. What you'll be driving * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Monitoring as Code - Managing your dashboards at scale](https://www.wearedevelopers.com/videos/753-monitoring-as-code-managing-your-dashboards-at-scale) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Making Data Warehouses fast. 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