• Architect and lead end to end tagging and tracking strategy across AXS digital properties; develop, build, test, and deploy tags, triggers, and variables through Adobe Data Collection, ensuring accurate and consistent data collection. • Provide day-to-day management of the data collection integrity, including evaluation, QA, and optimization • Serve as the primary analytics liaison with internal stakeholders (Product, Marketing, Engineering, Design) to orchestrate data collection to align with business needs in a way that ensures data accuracy and completeness. • Build data feeds and pipelines out of core collection systems (Adobe Analytics and Rudderstack) and partner with engineering teams to integrate data to downstream systems. • Own the day-to-day technical data Governance & Quality Assurance Audit analytics setup, validate tracking accuracy, and ensure data integrity across all platforms and environments. • Analyze web and mobile performance metrics using Adobe Analytics, or other digital platforms to identify trends, opportunities, and issues impacting engagement, traffic, and conversion. • Design and Build data feeds and pipelines as well as maintain dashboards and automated reports that provide actionable insights to leadership, Marketing, and Product teams; develop standardized KPIs and reporting cadence.

• Partner with Product, Analytics, and Engineering to understand data needs and translate ambiguous questions into clear, scalable data models • Define, build, and maintain core dbt models that transform raw product data into canonical, well-documented datasets • Own metric definitions and transformation logic to ensure consistency, accuracy, and trust across reporting and analysis • Establish and uphold data quality standards, testing, and expectations around freshness and reliability • Work closely with Product Analysts to enable faster, higher-quality insights and decision-making • Support data consumption in tools like Amplitude and Omni, ensuring data is intuitive and easy to self-serve • Act as a subject-matter expert for analytics engineering, guiding best practices and helping others solve data problems • Contribute to shaping the future direction of our data stack as product complexity and scale increase

• Develop and maintain capacity models for compute, storage, and network infrastructure across global environments. • Build and productionize advanced time‑series forecasts (e.g., ARIMA/ETS, Prophet, XGBoost/LightGBM) to predict demand, saturation points, and runway. • Conduct scenario modeling (“what‑if”) on deployment plans, workload changes, demand spikes, and hardware refresh strategies. • Analyze historical utilization to identify emerging risks, inefficiencies, and optimization opportunities. • Design, build, and maintain Python‑based data pipelines for ingesting, transforming, and validating large‑scale infrastructure telemetry. • Create ETL/ELT workflows to support analytics, modeling, and reporting. • Integrate data from observability platforms (e.g., Prometheus/Grafana), CMDB/asset systems, and internal services. • Develop APIs/services to expose forecast results and capacity signals to dashboards and tooling. • Build executive‑ready dashboards in Power BI (DAX, Power Query, custom visuals) and integrate real‑time forecasting outputs. • Deliver clear, compelling insights to engineering, operations, and finance leaders to support both strategic and tactical decision‑making. • Automate reporting workflows and ensure up‑to‑date visibility into runway, utilization, and risk posture. • Partner with engineering, operations, and finance teams to align capacity plans with growth, reliability, and cost objectives. • Establish standards for model governance, documentation, and data quality. • Drive continuous improvement of capacity planning systems, tooling, and analytics frameworks.

• Collaborate with engineers, product teams and analysts to develop data products that are precise and insightful. • Own the data product lifecycle, including designing tracking plans, developing data models, ELT pipelines, and self-serve data products. • Design and maintain an AI-enabled semantic layer that allows business users to interact with data through natural language using agentic AI tools. • Find creative ways to integrate AI into the Data lifecycle. • Be the data steward, ensuring data quality and consistent metrics.