Lead GTM Data Operations Analyst, AI Workflows

Klaviyo•San Francisco, CA

5d•Remote

About The Position

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements. The GTM Data Strategy & Operations function is being built from scratch, currently managed by a single leader and supported by offshore contractors. The operating model is AI-first, utilizing a multi-agent pipeline for data quality processes. This role will sit between AI systems and GTM data, operating, tuning, and extending the agentic data quality pipeline. The position involves owning the handoff between automated output and human review, managing quality and throughput with an offshore team. The successful candidate will run, evaluate, and improve AI agents, not build them from scratch.

Requirements

3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
Process design mindset with a bias toward measurable outcomes; strong written communication.

Nice To Haves

Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
Python for QA scripting, sampling, or light automation.
Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

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.