Data Analyst

Job Description Roles & Responsibilities Senior Experimentation Analyst - Owning high-stakes tests end to end. Analytics Engineer - Building the data layer and self-serve tooling the whole team relies on. What takes 4-5 years elsewhere happens in 1-2 here. Who You'll Work With: You'll sit within the Analytics team and collaborate closely across the organization: Senior Management: Your recommendations shape site and campaign decisions Digital Marketing Team: Tie ad platform data to on-site behavior across the full funnel CRO: Partner on hypothesis prioritization and test design Dev & QA: Translate winning tests into production and validate implementation Finance: Connect experimentation outcomes to revenue impact Customer Support: Surface post-purchase friction that feeds upstream test ideas Our team includes alumni from Apple, Deutsche Bank to name a few. What You'll Own: Analyze A/B tests end-to-end: what worked, what didn't, why and deliver decision-ready recommendations, not metrics readouts. Map user journeys across devices and traffic sources; pinpoint friction, drop-offs, and conversion leaks; convert them into prioritized, testable hypotheses. Dig into data at segment, device, source, and journey-step level to find the non-obvious drivers that top-line reporting misses. Build and maintain your own data pipelines across Shopify, GA4, Google Ads, and BigQuery: you don't wait in a queue to get the data you need. Contribute to a structured hypothesis pipeline: prioritize by impact, write clear test briefs, define success criteria upfront, and push proposals to execution. Orchestrate agentic AI workflows to accelerate analysis using SQL and Python as the foundation to direct, validate, and scale your analytical output. Collaborate with marketing, UX, and development to translate findings into site changes, campaign adjustments, and creative decisions. Desired Candidate Profile 3+ years in data analysis, experimentation, or CRO within e-commerce, D2C, or any high-traffic transactional website (fintech, marketplaces, SaaS, banking). Strong SQL: complex joins, window functions, CTEs across large datasets without relying on pre-built dashboards. Working Python (or R) for data manipulation, statistical analysis, and automation. Proven A/B testing experience: hypothesis design through statistical analysis to business recommendation; solid grasp of sample sizing, significance thresholds, and common pitfalls. Hands-on GA4 experience: event-based tracking, custom explorations, and the ability to tie GA4 data to other sources. Experience integrating ad platform data (Google Ads, Meta) with site analytics to see the full acquisition-to-conversion funnel. Ability to independently connect to and pull from multiple data sources (APIs, warehouses, flat files) without engineering support. Sharp analytical communication: complex findings distilled into concise recommendations for non-technical stakeholders. Proactive and curious: you spot testing opportunities and chase root causes before anyone asks. Good-to-Have: Experience with BigQuery or AWS data warehousing, including scheduled queries and data modeling. Familiarity with session replay and heatmap tools (FullStory, Hotjar) for qualitative behavioral analysis. Background in Looker, Tableau, or similar dashboarding tools. Next Steps: Click "Apply" to start your application today. Company Industry FurnitureFurnishingsHome Décor Department / Functional Area IT Software Keywords Data Analyst Get real-time job updates only on our App