As a Senior Manager of AI & Workforce Intelligence within the Trust & Infrastructure Business Operations team, you will own the design, development, and automation of intelligent systems that give operations leaders real-time visibility into the workforce — headcount, hiring pipelines, organizational health, and position lifecycle. You will be the technical and analytical bridge between raw, disparate data sources (Workday, finance systems, recruiting platforms, planning tools) and the polished, automated dashboards and insights that drive headcount decisions across a 5,000+ person engineering organization. This is not a role that exists today — you will build it from scratch. You'll define the data architecture, select and integrate AI/ML tooling, and create self-sustaining automation pipelines that eliminate manual reporting and surface predictive insights. You will work at the intersection of data engineering, AI/ML, and business operations — reporting to the VP of Trust & Infrastructure Centralized Operations and partnering daily with Finance, Recruiting, Employee Success, and Engineering leadership. The ideal candidate is deeply technical, obsessed with automation, fluent in AI/ML applications for business data, and energized by the challenge of turning messy, siloed workforce data into a single source of truth that leaders trust and act on. Responsibilities Workforce Data Platform & Automation • Design and build an end-to-end automated data pipeline that ingests, reconciles, and normalizes workforce data from multiple disparate systems (Workday, Tableau, Google Sheets, finance extracts, recruiting tools, planning spreadsheets) • Create self-healing, scheduled automation that detects data drift, validates freshness, and alerts on anomalies — eliminating manual data pulls and reconciliation • Own the full lifecycle of position and headcount data: from requisition creation through offer acceptance, backfill, transfer, and attrition AI-Powered Insights & Predictive Analytics • Apply AI/ML models to forecast attrition risk, hiring pipeline health, time-to-fill, and organizational capacity gaps • Build intelligent alerting systems that proactively surface workforce risks (e.g., span of control imbalances, unfilled critical roles approaching SLA, concentration risk in key functions) • Leverage large language models and agentic AI to automate narrative reporting, summarize organizational changes, and generate executive briefings from raw data Dashboard Design & Delivery • Design, build, and maintain a suite of operational dashboards (Google App Script, Tableau, Google Sheets, web-based) that serve as the single source of truth for headcount management across Trust & Infrastructure • Create role-specific views — executive summaries for EVPs, SVPs, VPs, operational detail for managers, compliance and audit views • Ensure dashboards are self-service, real-time (or near-real-time), and require zero manual intervention to stay current Headcount Management Lifecycle Automation • Automate the end-to-end headcount management lifecycle: position creation, approval routing, requisition tracking, offer management, onboarding triggers, and backfill initiation • Build workflow automation that connects planning decisions to downstream systems — when a leader approves a new headcount, the requisition, budget allocation, and recruiting intake should flow automatically • Implement change detection and audit trails that track every position action with full provenance Cross-Functional Partnership & Stakeholder Enablement • Partner with Finance, Recruiting, Employee Success, and Engineering leadership to understand their data needs and deliver solutions that reduce their operational burden • Serve as the subject matter expert on workforce data integrity — rationalizing conflicting sources, challenging inaccurate data, and establishing governance standards • Train and enable operations team members to leverage the dashboards and automation you build Innovation & Continuous Improvement • Stay current with emerging AI/ML capabilities, automation frameworks, and data engineering best practices — continuously evaluating new tools and approaches • Bring a builder's mindset: prototype rapidly, validate with users, iterate, and scale what works • Document architectures, data flows, and runbooks so that the systems you build are maintainable and extensible Required Qualifications • 8+ years of experience in data engineering, business intelligence, workforce analytics, or a related technical operations role • 3+ years hands-on experience building automated data pipelines that integrate multiple disparate data sources (APIs, flat files, databases, SaaS exports) • Demonstrated proficiency with modern BI/dashboard tooling (Tableau, Looker, or equivalent) including designing dashboards from scratch • Strong programming skills in Python, SQL, or similar — with experience building production-grade automation (not just ad hoc scripts) • Practical experience applying AI/ML to business problems — whether predictive modeling, NLP/LLM integration, anomaly detection, or intelligent automation • Experience with workforce data systems (Workday, SuccessFactors, or equivalent HRIS) and understanding of headcount management processes • Track record of building something from zero — comfortable with ambiguity, able to define scope, and energized by greenfield work • Excellent communication skills with the ability to translate technical approaches into business value for non-technical stakeholder Preferred Qualifications • Experience with agentic AI frameworks, LLM-based automation, or AI-assisted workflow orchestration • Familiarity with Google Apps Script, Google Workspace APIs, or similar integration platforms • Experience with scheduling/orchestration tools (Airflow, cron-based pipelines, event-driven architectures) • Background in HR analytics, people analytics, or workforce planning within a large technology organization • Experience with Salesforce's internal systems and data ecosystem • Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Business Analytics, or a related field At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $143,400 - $216,900 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.