GTM DataOps Lead: AI-Driven Pipeline & QA

United States Digital Space LLC; Boston (MA); On-site; USD 90,000 - 130,000; Full time

Job summary

Klaviyos in Boston is seeking an on-site Data Ops professional to operate and extend our agentic GTM data quality pipeline, coordinating with offshore contractors to ensure reliability and throughput. You will tune detection rules, manage AI-assisted enrichment workflows, and balance automated outputs with human review to improve data quality and coverage for enterprise GTM data.

The role requires hands-on ops experience, collaboration with GTM Systems and Data Engineering, and a bias toward

Qualifications

• 3–6 years in Data Ops, Sales Ops or GTM Ops with data-quality ownership.
• Proficiency with Snowflake and SFDC.
• Experience with Claude Code or similar LLM tooling.
• Experience designing AI-assisted enrichment workflows.
• Comfort in CLI: tmux, shell scripts, log analysis.
• Strong written communication.

Responsibilities

• Agent Pipeline Operations: Run and monitor production pipeline sessions; diagnose and resolve failures.
• Agent Tuning & Improvement: Refine detection rules and confidence thresholds; evaluate accuracy by segment.
• Sentinel Offshore Resolution Loop: Manage handoff to offshore triage queues; monitor quality and throughput.
• Data Quality & Enrichment Operations: Maintain staging fields; design AI-assisted enrichment workflows.
• Cross-Functional Partnership: Collaborate with GTM Systems, Data Engineering and Reporting.

Skills

Data ops

Data quality ownership

SQL (Snowflake)

SFDC configuration

Claude Code / LLM tooling

Prompt engineering

Tools

Snowflake

SFDC

Claude Code

Job description

Klaviyos in Boston is seeking an on-site Data Ops professional to operate and extend our agentic GTM data quality pipeline, coordinating with offshore contractors to ensure reliability and throughput. You will tune detection rules, manage AI-assisted enrichment workflows, and balance automated outputs with human review to improve data quality and coverage for enterprise GTM data.

The role requires hands-on ops experience, collaboration with GTM Systems and Data Engineering, and a bias toward