Remote Data Analytics Analyst

Data Analyst HYBRID
- MUST LIVE IN MEXICO CITY , MEXICO 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. 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? 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. 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.
· Challenge assumptions in business requests where data tells a different story. Design and support A/B test experiments with statistically valid methodology — including hypothesis definition, sample sizing, success and guardrail metric selection, and results analysis. Google Analytics & Data Collection
· Act as the team's primary Google Analytics subject matter expert. 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.
· Proactively maintain the accuracy and consistency of production Explores and dashboards as underlying data warehouse structures change. 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. 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.
· 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. 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, hallu