Lead GTM Data Operations Analyst, AI Workflows

Klaviyo is a company that empowers creators to own their destiny by making first-party data accessible and actionable. The Lead GTM Data Operations Analyst, AI Workflows role involves operating and enhancing the agentic data quality pipeline, ensuring it runs reliably and efficiently while managing the handoff between automated outputs and human review.

Responsibilities:

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership
• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making
• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision
• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve
• GTM Systems (SFDC): field configuration, permission sets, automation, flows
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding)
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements