• Architect and implement the unified GTM data spine across CRM, marketing automation, product telemetry, billing, enrichment providers, partner data, and the warehouse. • Establish identity resolution, deduplication, and source-of-truth logic across systems. • Define canonical schemas that we’ll use to operate the business, enable AI across teams and drive results. • Build scalable ELT/ETL pipelines and orchestration workflows tailored to revenue activation. • Implement governance, lineage, access control, and quality monitoring for GTM data assets. • Partner closely with the BI / enterprise data team to align on shared infrastructure while owning GTM-specific models and activation layers. • Develop and productionize segmentation and scoring models (ICP scoring, ABM prioritization, expansion propensity, trial health, pipeline likelihood). • Apply statistical and machine learning techniques to improve scoring, targeting, routing, and revenue predictability. • Design experimentation and measurement frameworks for GTM programs. • Operationalize predictive outputs directly into GTM systems via agents, reverse ETL, or custom integration (Salesforce workflows, outbound automation, lifecycle programs, ABM platforms, partner programs). • Architect customer and prospect intelligence systems integrating first-party, enrichment, and ecosystem signals. • Develop frameworks for ecosystem intelligence (partner influence, marketplace signals, derived demand). • Enable real-time or near-real-time signal activation to sales and marketing teams. • Design structured data systems that power future AI-driven revenue workflows. • Manage and direct external agency/consulting partners executing against the GTM data roadmap. • Establish architectural standards and technical review processes. • Define the build vs. buy strategy for GTM data systems. • Develop the long-term roadmap for in-house data capability.

• Data Pipeline Engineering: Architect, build, and maintain complex, real-time, and batch data pipelines using Azure Data Factory, Python/PySpark, and Databricks. • Architecture & Modelling: Design and implement modern data warehouse solutions, data models, and data lakes, optimizing for performance and scalability. • Data Ingestion & Integration: Ingest, cleanse, and transform data from diverse sources into usable data structures for analytics. • Security & Governance: Implement security features, including role-based access control (RBAC), data encryption, and governance via Azure Purview.

• Data Pipeline Development: Design, build, and optimize robust ETL/ELT pipelines using Azure Databricks, Spark, and SQL. • Data Processing & Transformation: Utilize Python, PySpark, and SQL to clean, transform, and aggregate complex data for analytics. • Azure Data Lake Management: Manage and optimize data storage and retrieval in Azure Data Lake Storage (ADLS) Gen2. • Delta Lake Implementation: Implement Delta Lake for ACID compliance, data versioning, and high-performance data lake operations. • Integration with Azure Services: Integrate Databricks with Azure Data Factory for orchestration, Azure Synapse Analytics for warehousing, and Azure Key Vault for security. • Performance Optimization: Monitor, troubleshoot, and optimize Databricks clusters and spark jobs to manage costs and performance. • Data Security & Governance: Implement Role-Based Access Control (RBAC), data encryption, and data lineage tracking. • Requirements Collaboration: Work with data scientists and analysts to support machine learning models and business intelligence (BI) reporting.

• Own and architect the end-to-end reporting lifecycle in the diamond matching project • Inherit a replicated production environment • Transform raw Python application data into a high-performance analytics layer • Bridge the gap between backend data structures and business-ready visualizations