Senior Data Analyst

About the role

Senior Data Analyst HYBRID- MUST WORK EST( Eastern Standard Time, United States) About Tillster Headquartered in the USA, Tillster is the global leader in digital ordering and customer engagement solutions. For over a decade we have developed revolutionary self-service, ordering and payments solutions - for mobile, tablet, online, kiosk, call center, and more - creating personalized interactions based on consumer preferences, language, and currency. Our platform is compatible with 15+ unique POS systems, representing over 90% coverage in multi-unit restaurants. We offer one platform: one scalable, enterprise class solution - to create world-class digital engagement solutions. Our mission and passion are one in the same: Empower restaurants and consumers to engage and transact anywhere, anytime, and from any device - one consumer at a time, one order at a time, billions of times over. In doing so, together we are transforming e-commerce in restaurants and making the till grow for Tillster and our customers. Are you a passionate data analytics with a knack for data? Job Duties Analytics & Insight Delivery - Own the end-to-end delivery of complex analytical initiatives - from framing the business question and scoping the analysis through execution, presentation, and follow-through on stakeholder action. Do not wait to be asked; proactively surface KPI trends, anomalies, opportunities, and risks before stakeholders encounter them. - Support ad-hoc analysis for Tillster customers, executives, and internal stakeholders - going beyond data retrieval to provide interpretive framing and actionable recommendations with a clear point of view. - Operate as a trusted analytics advisor to Marketing, Product, Finance, Engineering, Account Management, and Support/Operations. Challenge assumptions in business requests where data tells a different story. Translate complex findings into decision-relevant narratives. - Design and support A/B test experiments with statistically valid methodology - including hypothesis definition, sample sizing, success and guardrail metric selection, and results analysis. Communicate findings clearly to Product and Marketing stakeholders. Google Analytics & Data Collection - Act as the team's primary Google Analytics subject matter expert. Own GA4 reporting, custom dimensions and metrics design, tracking plan validation, and GA-to-warehouse integration. Advise on tagging strategy for new product features and platform launches in partnership with the Lead MarTech Solutions Architect. - BI Development & Semantic Layer - Design, build, and maintain high-performing Explores and dashboards in Looker for business stakeholders including Marketing, Product, Finance, Engineering, Account Management, and Support/Operations - owning deliverables end-to-end with minimal re-work. - Develop LookML for owned domains to a high quality standard - including complex dimensions, measures, derived tables, and Explore configurations. Ensure all LookML is documented, tested, and consistent with organisation-wide semantic layer standards. - Ensure metric definitions in owned domains are canonical, unambiguous, and consistent with the definitions governed by the Analytics Engineering team. Flag and resolve discrepancies between business intent and semantic layer implementation. - Expand self-service analytics capabilities - enabling non-technical users across Marketing, Customer Success, and Finance to answer their own questions independently, reducing ad-hoc request volume to the team. Target coverage across ≥2 new business units per half-year. - Proactively maintain the accuracy and consistency of production Explores and dashboards as underlying data warehouse structures change. Identify discrepancies before stakeholders encounter them. Data Quality & Platform Collaboration - Own the accuracy and freshness of all data products in scope. Proactively identify and investigate data quality issues - with root-cause evidence - and partner with Analytics Engineers and Data Engineers to resolve upstream problems promptly. - Write high-quality, performant SQL - including complex window functions and CTEs - and collaborate with Data Engineers on query optimisation, compute cost efficiency, and derived table design for owned analytical workloads. - Collaborate with Analytics Engineers on LookML architecture and semantic layer design for owned domains - providing analytical requirements that inform model design and validating that data models correctly reflect business logic. - Partner with the Senior BI & AI Analyst on customer analytics definitions for segmentation, LTV, and churn use cases. Consume ML model outputs from the Data Science team and surface them accurately in dashboards and analytical narratives. - Understand the structure of data pipelines feeding owned dashboards sufficiently to diagnose data freshness and quality issues, escalate to the correct team, and communicate resolution timelines to stakeholders. S

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