Maschal Masch Malek

About Maschal Masch Malek

Strategic Go-to-Market Systems professional specializing in Salesforce ecosystems, data enrichment, and AI-driven Revenue and Marketing Technology solutions. I lead global implementations of innovative sales technologies, manage complex cross-functional projects, and optimize processes to significantly enhance operational efficiency and revenue impact.Passionate about leveraging AI to empower Sales teams, I frequently speak on data preparedness, AI, and revenue operations, recently sharing insights at Salesforce\'s Dreamforce conference. I\'ve also participated in industry webinars and podcasts, collaborating closely with leaders at Traction Complete and RevOps Co-op to discuss best practices in RevOps and strategic data usage.I thrive on solving complex problems, driving measurable improvements, and sharing knowledge within the RevOps community. Always open to connect, exchange insights, or discuss ideas in RevOps, AI integration, or Salesforce innovation.

Experience

1. Product Manager, Enterprise Ai

   Asana

   Sep 2025 — Present · San Francisco, CA, US

   Own end-to-end strategy, roadmap, and delivery of ORBIT—an AI-native revenue automation platform that orchestrates account intelligence, prospect research, signal-based outreach, and lifecycle workflows across Salesforce, Databricks, Workato, SnapLogic, and OpenAI for 300+ global sales users.• Designed the system architecture for ORBIT’s POV (Point of View) engine—a multi-stage automation pipeline that aggregates CRM data (Accounts, Opportunities, Tasks, Trials, Cases), call transcripts, hiring signals, competitor research, and news enrichment, then generates AI-powered account narratives and next-best-actions delivered directly into Salesforce.• Built dual execution modes: on-demand (user-triggered refresh via custom SFDC buttons and Workato listeners) and scheduled batch processing (Databricks Python notebooks with Airflow orchestration), enabling both real-time seller workflows and nightly bulk POV generation at scale.• Defined the LLM integration layer: prompt and output schema design versioned in GitHub, web search and enrichment functions, HTML template rendering, and multi-language instruction sets supporting localization across 19 markets.• Architected the data model spanning Databricks (account prioritization scoring, prospect identification, POV job logging) and Salesforce (custom fields for POV content, justification HTML, next-best-actions, and refresh triggers)—with daily and weekly refresh cadences across 6+ pipeline tables.• Designed the account and prospect scoring engine in Databricks: weekly account prioritization scores, daily prospect surfacing (100 accounts per user: 50 new business, 50 expansion), and ZoomInfo company mapping for enrichment correlation.• Built observability and reliability infrastructure: Slack alerting on errors (#orbit-alerts), POV job logging for audit trails, and adoption dashboards tracking generation volume, signal health, and system performance.