• Own end-to-end analytics solutions from definition to data architecture and data visualization. • Streamline single source of truth insights for business in an analytics as code environment. • Drive impact to business stakeholders and balance between technical ownership and business partnership.

• Take ownership of our BI ecosystem and drive high-impact improvements in data reliability, performance, and business insights. • This role sits at the intersection of data engineering, analytics, and business strategy, working closely with Marketing and cross-functional teams to ensure data is not only accurate — but actionable. • Play a key role in transforming how the company uses data to make decisions. • Own and improve the reliability and performance of dashboards and reporting systems • Design and optimize data models and SQL transformations in the data warehouse • Build and maintain scalable BI solutions using Tableau and cloud data platforms • Implement data quality checks, monitoring, and anomaly detection systems • Partner with stakeholders to translate business needs into robust data products • Improve query performance and reduce reporting inefficiencies • Define and standardize KPI frameworks, especially for Marketing metrics • Ensure documentation, ownership, and governance across all BI assets

• Define and own the multi-year roadmap for the data platform, aligning investments in infrastructure, tooling, and headcount with business strategy. • Lead and grow two high-performing teams—Data Engineering and Analytics Engineering—cultivating a collaborative, feedback-rich environment with clear career pathways. • Architect and oversee scalable data pipelines across ingestion, transformation, orchestration, and delivery, for both batch and streaming use cases. • Champion best practices in analytics engineering, including semantic layer design, dbt modelling standards, data contracts, and metrics governance. • Partner with Data & Decision Science, Product, Finance, and Commercial teams to deliver high-quality, self-serve data solutions aligned to business needs. • Ensure data platform reliability, observability, SLAs, and incident response, treating the platform as a product with real users. • Drive vendor and tool evaluations for the modern data stack (cloud warehouse, orchestration, cataloging, transformation, reverse ETL, etc.). • Set and enforce data quality, documentation, and governance standards to build trust across the business. • Champion use of AI coding assistants and LLM-powered tooling (e.g. Cursor, GitHub Copilot, Claude) to accelerate delivery and reduce toil. • Implement AI-native patterns—LLM-generated documentation, anomaly detection, data quality monitoring, and automated root-cause analysis. • Prototype NL-to-SQL and AI-powered BI tools to empower self-serve analytics for non-technical users. • Build foundational data infrastructure (feature stores, vector stores, model metadata, evaluation datasets) to enable AI and ML experimentation and scale.

• Architect and maintain robust, modular data models in Snowflake using dbt, following industry-standard modeling methodologies (e.g., Kimball). • Write and tune advanced SQL to ensure optimal query performance, cost-efficiency, and resource management within the Snowflake environment. • Implement and manage automated testing, monitoring, and alerting frameworks to ensure data accuracy, freshness, and lineage. • Partner with business units to define KPIs, capture requirements, and translate business logic into technical data specifications. • Own the full data lifecycle from ingestion to production-grade data marts and strategic BI visualizations and dashboard building. • Apply software engineering best practices to data development, including version control (Git), CI/CD, and detailed technical documentation. • Continuous refactoring of legacy code and data structures to improve maintainability and scalability of the analytics stack.