Data Analyst, People Analytics

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.

The People Analytics team at Lyft exists to build data trust — turning people data into clear, reliable insights that help our HR Business Partners, talent leaders, and executives make better decisions. We're a small, high-impact team in the middle of a meaningful BI transformation: we recently moved to ThoughtSpot as our primary analytics platform, and we're not just replicating old dashboards — we're rethinking what self-service analytics looks like for a People team. That includes exploring ThoughtSpot's AI capabilities to surface proactive insights, enable natural language querying, and reduce the friction between a business question and a data answer. There's real greenfield work here, and we're looking for someone who wants to help define what AI-powered people analytics looks like at Lyft.

We're looking for a Data Analyst to join our People Analytics team, based in Toronto. You'll report to the Senior Manager, People Analytics and serve as the first point of contact for incoming data requests across the People organization — triaging, scoping, and delivering on analytical needs ranging from quick ad-hoc pulls to fully built ThoughtSpot dashboards.

This role is well-timed: you'll be joining as we complete our migration from Tableau to ThoughtSpot, which means you'll have genuine influence over how we build our reporting layer and push into ThoughtSpot's AI features — think natural language search, AI-generated insights, and proactive anomaly detection applied to people data. This isn't a "maintain the dashboard" role. It's a chance to help build something new. You'll be a self-starter who is equally comfortable writing SQL and presenting findings to a VP.

• →Serve as the first point of contact for incoming data requests from across the People organization — scoping needs, setting expectations, and delivering high-quality analytical outputs
• →Build and maintain ThoughtSpot dashboards and liveboards for a broad stakeholder audience including recruiters, People Team members, HRBPs, managers, and executives
• →Explore and operationalize ThoughtSpot's AI capabilities — including Spotter and AI Highlights — to surface proactive insights for People stakeholders who may not know what question to ask yet
• →Build and administer survey programs in Qualtrics — including survey design, distribution logic, and close-out — and translate results into dashboards and ad-hoc analyses that inform People team decisions
• →Partner with Talent and People team leads to define key metrics and help them tell meaningful stories with their data
• →Translate analytical findings into clear, actionable insights for both People leadership and senior executives
• →Communicate fluently across technical and non-technical audiences — from SQL-level data discussions to exec-facing summaries
• →Collaborate with systems and operations partners to improve upstream data quality and enable better downstream analytics
• →Pull together ad-hoc datasets from disparate sources to answer discrete analytical questions quickly and accurately

• 4+ years of experience in data or business analytics
• 3+ years of SQL experience, with comfort pulling from complex, multi-source data environments
• Experience with ThoughtSpot or a comparable modern BI platform; enthusiasm for AI-assisted analytics and a curiosity about what those tools unlock for people data specifically
• Familiarity with Workday or a comparable HCM (understanding the business processes and data model, not just surface-level reporting)
• Familiarity with Greenhouse or a comparable ATS preferred
• Experience with Qualtrics or a comparable survey platform, including hands-on program administration and the ability to move from raw survey data to a clean, stakeholder-ready analysis
• Experience building and maintaining analytical models that others rely on
• Understanding of the nuances of people and recruiting data — org structures, headcount definitions, privacy considerations, and the ways these datasets can surprise you