• Phase 1 - Set up the foundation & Retention Analytics: Define the Data Platform, setting up a cloud data warehouse (Snowflake), DBT (core/cloud), repositories, data alerting, BI system and AI based analytics engineering development working model (claude/cursor/conductor etc) • Ingest core data sources: Atlassian Marketplace API, HubSpot, and product databases (Postgres, MongoDB) • Build the SSOT data layer using dbt: clean, tested, version-controlled transformations • Deliver trusted retention and churn analytics (active customers, logo churn, revenue churn, Net Revenue Retention) • Establish a metric glossary with clear, agreed-upon definitions for core business metrics • Set up pipeline monitoring, data quality checks, and alerting so broken data is caught before it reaches a dashboard • Phase 2 - Build LTV, GTM & Product metrics to help us scale: Build LTV and unit economics models (CAC, ROAS, payback period) by connecting marketing spend data with downstream Marketplace and CRM data • Model the full customer lifecycle: trial → paid → expansion → churn, with cohort and conversion analytics • Fix and automate the Atlassian ↔ HubSpot data sync (reverse ETL), replacing fragile custom scripts with a reliable, maintainable solution • Identify cross-sell opportunities by modeling multi-product adoption across the 70K+ customer base • Build and maintain key dashboards for leadership, marketing, product, and customer success

• 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.