About this role
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
Data is at the heart of Lyft Business's products and decision-making. We're looking to hire a Data Analyst to build the tools and analytics that drive revenue for the sales organization behind Lyft's B2B portfolio — a multi-billion-dollar book of business spanning healthcare, business travel, corporate commute, automotive, and transit. This team has real say in what gets built.
You will be the analytical partner to our Sales and account teams — someone who can translate findings into a clear narrative for both technical and non-technical audiences.
Responsibilities:
• Build the tools and analytics sales leaders use to grow their accounts — pacing views, account-health signals, territory and segment views that change what a rep or manager does next
• Field requests from Sales, Product, Finance, Legal, leadership, and external stakeholders, and decide what's worth building, what's worth a sharp piece of analysis, and what's out of scope
• Build and maintain production data pipelines, and automate recurring workflows, with documentation and validation clear enough that a teammate — or an AI agent — can use them without asking you first
• Define what the team's core metrics mean and where they live, so every dashboard, report, and tool returns the same answer to the same question
• Contribute to the internal tools and semantic layer — prompts, context, and guardrails — that let stakeholders query B2B data in natural language and trust what comes back
• Translate insights into a cohesive narrative and present technical findings to senior leadership and non-technical audiences
Experience:
• Degree (or related work experience) with a focus in analytics, statistics, economics, computer science, or other quantitative fields
• 3+ years of experience in a data analytics or business intelligence role, ideally supporting a sales, go-to-market, or B2B organization
• Proficiency in SQL — able to read, write, and review structured, efficient queries on large data sets, whether you wrote them yourself or an AI coding assistant did; you own the correctness of every query and result before it reaches stakeholders
• Experience building production data assets — pipelines, data models, or orchestrated tables (Airflow, dbt, or similar) — not only ad-hoc analysis
• Proficiency in a data visualization tool like Tableau, Hex, Mode, or similar
• Experience with sales or account analytics — pipeline, bookings, pacing to target, account segmentation — is a strong plus
• Experience with Python for data analysis and automation is a strong plus
• Commercial instinct paired with a builder's instinct — a track record of turning a recurring request into a durable tool or dataset instead of answering it one more time
• A track record of influencing business decisions through analysis, and an own