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Senior Data AnalystTillster • United States
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Senior Data Analyst

Tillster
  • US
    United States
  • US
    United States
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Über

Senior Data Analyst HYBRID- MUST WORK EST( Eastern Standard Time, United States)
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. Standards, Mentorship & Team Development · Own coding standards and analytical best practices for the team — including SQL style guides, LookML conventions, documentation requirements, and PR review processes. Conduct structured code reviews that are substantive, consistent, and designed to build capability in Junior Analysts. · Mentor Junior Data Analysts through paired analysis, direct feedback, and structured review. Track improvement over time. Escalate development needs or blockers to the Manager. · Build and contribute reusable assets to shared team libraries — including SQL query patterns, LookML components, analysis frameworks, and documentation templates — reducing time-to-delivery for the entire team, not just own work. · Contribute to at least one standards update, governance policy, or best-practice guide per year. AI-Augmented Analytics & Self-Serve Agent Development · Apply LLM and AI tools (Gemini, GPT-4, Cortex Analyst, or equivalent) hands-on to accelerate analytics workflows — including automated insight narration, anomaly detection and explanation, and AI-assisted dashboard annotation. Practical delivered experience required; theoretical familiarity is not sufficient. · Design and build self-serve analytics agents that enable non-technical stakeholders in Marketing, Customer Success, Finance, and Account Management to query data and surface insights in natural language — without raising tickets or requiring analyst intervention. Own the full lifecycle from prompt design through output validation and iteration. · Configure Cortex Analyst or equivalent AI-assisted BI platforms to extend self-service beyond traditional dashboard interaction — including semantic model templates and YAML definitions that produce accurate, governed responses to unstructured business questions. · Define quality standards for AI-generated analytical outputs — including accuracy validation, hallucination detection, and a review process before AI-generated insights are shared externally or with executives. · Prototype and propose AI tooling that reduces repetitive analytical work for the team. Present prototypes to the Manager with a clear build-vs-buy recommendation and documented lessons learned that contribute to the team's shared AI analytics playbook. Skills Required with Years of Experience Core Technical Skills (5-7 years total experience) SQL & Data Analysis - 4-5 years · Advanced SQL (window functions, CTEs, query optimization) · Complex analytical queries and performance tuning Business Intelligence Tools - 3-4 years · Looker expert (LookML development, Explores, dashboards) · Data visualization and self-service analytics design Google Analytics - 2-3 years · GA4 implementation, configuration, and reporting · Tracking strategy and warehouse integration Python/Programming - 1-2 years (preferred) · Data manipulation and automation · Version control (Git) and code review Statistical & Analytical Skills (3-5 years) A/B Testing & Experimentation - 2-3 years Experimental design and statistical testing Hypothesis formulation and results interpretation Statistical Analysis - 3-4 years Descriptive and inferential statistics Trend analysis and anomaly detection AI & Emerging Technologies (1-2 years) AI-Augmented Analytics - 1-2 years Hands-on LLM usage (GPT-4, Gemini, Cortex Analyst) AI agent development and prompt engineering AI output validation and quality standards Business & Communication Skills (4-6 years) Stakeholder Management - 4-5 years Cross-functional collaboration (Marketing, Product, Finance, Engineering) Executive communication and presentation Trusted advisor relationships Business Analytics - 3-4 years Customer analytics (segmentation, LTV, churn) KPI development and metric definition Translating business questions to analytical frameworks Leadership & Mentorship (2-3 years
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