operations
Posted 6 days agoLead GTM Data Operations Analyst, AI Workflows
at Klaviyo
San Francisco, United StatesOn-site
Responsibilities
- Own the handoff between automated output and human review, managing quality and throughput with our offshore team.
- 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. Agent Tuning & Improvement
- 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. Sentinel → Offshore Resolution Loop
- 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. Data Quality & Enrichment Operations
- 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.
- Process design mindset with a bias toward measurable outcomes; strong written communication. Strong Plus •
Requirements
- GTM Data Strategy & Operations stood up from scratch with no predecessor.
- The operating model is deliberately agentic AI–first : a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale.
- 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. Role Summary
- Sit between AI systems and GTM data.
- You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better. Core
- GTM Systems (SFDC): field configuration, permission sets, automation, flows.
- Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.
- 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.
- 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). Tool Stack
- Core: Snowflake (SQL), SFDC, Claude Code, Clay
- Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
- Nice to Have: Python, SOQL, prompt engineering frameworks
Benefits
- This is not a future-state vision, these agents are live and processing enterprise account families in production today.
- Our salary range reflects the cost of labor across various U.S. geographic markets.
- The range displayed below reflects the minimum and maximum target salaries for the position across all our US locations.
- The base salary offered for this position is determined by several factors, including the applicant’s job-related skills, relevant experience, education or training, and work location.
- In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for sales and customer success roles, equity, sign-on payments, and a comprehensive range of health, welfare, and wellbeing
- Your recruiter can provide more details about the specific salary/OTE range for your preferred location during the hiring process.
- Base Pay Range For US Locations: $124,000 — $186,000 USD
Contact
- Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.
Additional details
- At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day.
- We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements.
- If you’re a close but not exact match with the description, we hope you’ll still consider applying.
- Today the function runs on three offshore contractors and zero FTEs , managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).
- The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams.
- The function currently covers only core Tier‑1 fields.
- Dozens of account, contact, and lead signals remain unaddressed.
- Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.
- Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve. Cross-Functional Partnership
- Data Engineering: source availability, ID mapping, lineage (no pipeline coding).