H-1B Visa Data Operations Analyst Jobs
Data Operations Analyst roles qualify for H-1B sponsorship as specialty occupations requiring at least a bachelor's degree in a directly related field such as data science, statistics, or information systems. Employers file the Labor Condition Application with DOL before petitioning USCIS, and the 85,000-slot annual cap means timing your job search around the April registration window matters.
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Get Access To All JobsINTRODUCTION
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. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.
Why This Role, Why Now
GTM Data Strategy & Operations stood up from scratch with no predecessor. 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 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 is not a future-state vision, these agents are live and processing enterprise account families in production today.
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.
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. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don't build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.
Core Responsibilities
Agent Pipeline Operations
- 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.
- Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.
Cross-Functional Partnership
- 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.
What to Expect
This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You'll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.
Success Metrics (6–12 Months)
Pipeline Reliability
- Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
- Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).
Detection & Resolution Quality
- Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
- Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
- False-positive rate decreases through feedback-loop refinement.
Data Quality Outcomes
- Tier-1 field fill-rates: Country 95%; Vertical 90% at 85% sampled accuracy; Revenue bands 90%.
- Hierarchy coverage 65–80%+ across target segments.
- Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.
Qualifications
Required
- 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.
Strong Plus
- 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
- Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
- Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
- Nice to Have: Python, SOQL, prompt engineering frameworks
- AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.
This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance.
Get to Know Klaviyo
We're Klaviyo (pronounced clay-vee-oh). We empower creators to own their destiny by making first-party data accessible and actionable like never before. We see limitless potential for the technology we're developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you're ready to do the best work of your career, where you'll be welcomed as your whole self from day one and supported with generous benefits, we hope you'll join us.
AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human-in-the-loop). We provide accommodations as needed.
By participating in Klaviyo's interview process, you acknowledge that you have read, understood, and will adhere to our Guidelines for using AI in the Klaviyo interview Process. For more information about how we process your personal data, see our Job Applicant Privacy Notice.
Klaviyo is committed to a policy of equal opportunity and non-discrimination. We do not discriminate on the basis of race, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law.
IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls.
By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application. You can find our Job Applicant Privacy Notice here and here (FR).
INTRODUCTION
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. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.
Why This Role, Why Now
GTM Data Strategy & Operations stood up from scratch with no predecessor. 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 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 is not a future-state vision, these agents are live and processing enterprise account families in production today.
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.
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. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don't build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.
Core Responsibilities
Agent Pipeline Operations
- 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.
- Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.
Cross-Functional Partnership
- 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.
What to Expect
This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You'll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.
Success Metrics (6–12 Months)
Pipeline Reliability
- Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
- Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).
Detection & Resolution Quality
- Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
- Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
- False-positive rate decreases through feedback-loop refinement.
Data Quality Outcomes
- Tier-1 field fill-rates: Country 95%; Vertical 90% at 85% sampled accuracy; Revenue bands 90%.
- Hierarchy coverage 65–80%+ across target segments.
- Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.
Qualifications
Required
- 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.
Strong Plus
- 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
- Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
- Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
- Nice to Have: Python, SOQL, prompt engineering frameworks
- AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.
This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance.
Get to Know Klaviyo
We're Klaviyo (pronounced clay-vee-oh). We empower creators to own their destiny by making first-party data accessible and actionable like never before. We see limitless potential for the technology we're developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you're ready to do the best work of your career, where you'll be welcomed as your whole self from day one and supported with generous benefits, we hope you'll join us.
AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human-in-the-loop). We provide accommodations as needed.
By participating in Klaviyo's interview process, you acknowledge that you have read, understood, and will adhere to our Guidelines for using AI in the Klaviyo interview Process. For more information about how we process your personal data, see our Job Applicant Privacy Notice.
Klaviyo is committed to a policy of equal opportunity and non-discrimination. We do not discriminate on the basis of race, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law.
IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls.
By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application. You can find our Job Applicant Privacy Notice here and here (FR).
See all 971+ Data Operations Analyst jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Operations Analyst roles.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Data Operations Analyst
Verify your degree field matches the role
H-1B specialty occupation requires a degree in a field directly related to data operations work. A computer science or statistics degree maps cleanly, but a general business degree may trigger an RFE. Pull the O*NET profile for Data Operations Analyst to see which degree fields USCIS considers directly related.
Target employers with active LCA filing history
Search DOL's OFLC Wage Search to confirm a company has filed LCAs for data analyst roles before you apply. Employers who've certified LCAs recently already understand the process, which shortens the time from offer to petition filing.
Use Migrate Mate to find verified H-1B sponsors
Filter your Data Operations Analyst search on Migrate Mate to surface employers with confirmed H-1B filing history for this occupation code. That cuts out companies where you'd be the first sponsored hire, reducing risk at the offer stage.
Align your start date with the cap timeline
Cap-subject H-1B petitions have an October 1 earliest start date. If you're graduating in May or finishing OPT, confirm your employer can bridge the gap. Cap-exempt employers at nonprofits or universities can start you immediately regardless of the fiscal year cycle.
Ask employers about prevailing wage level before accepting
Your employer's LCA must certify a wage at or above the DOL prevailing wage for your job title and location. Data operations roles in tier-1 metro markets carry higher wage floors. Confirm which wage level they're filing at during negotiation, not after you've signed.
Prepare a job duties memo before the petition is filed
USCIS scrutinizes data analyst petitions for specialty occupation proof. Work with your employer's counsel to document that your daily tasks require application of specialized degree-level knowledge, not just general analytical skills. A detailed duties memo reduces RFE risk significantly.
Data Operations Analyst jobs are hiring across the US. Find yours.
Find Data Operations Analyst JobsData Operations Analyst H-1B Visa: Frequently Asked Questions
Does a Data Operations Analyst role qualify as an H-1B specialty occupation?
Yes, provided the position requires at least a bachelor's degree in a directly related field such as data science, information systems, statistics, or computer science. The employer must document in the petition that the role's core duties require theoretical and practical application of that degree-level knowledge. Generalist roles where any bachelor's degree suffices are more likely to receive an RFE from USCIS.
How do I find Data Operations Analyst employers who actually sponsor H-1B visas?
Search on Migrate Mate, which surfaces employers with verified H-1B LCA filing history filtered by occupation. You can also cross-reference DOL's OFLC Wage Search to see which companies have certified LCAs for data analyst roles in your target city. Prioritizing employers with prior filings in this occupation reduces the risk of sponsorship falling through after an offer.
What happens to my H-1B status if my Data Operations Analyst role changes significantly after approval?
A material change in job duties, worksite location, or employer requires an amended H-1B petition before the change takes effect. If your responsibilities shift from data pipeline management to primarily product management, for example, the specialty occupation basis may change. Your employer needs to file an amended I-129 with USCIS to document the updated role.
Can I switch employers while on H-1B as a Data Operations Analyst?
Yes. Under H-1B portability rules established by AC21, you can start working for a new employer as soon as they file an H-1B transfer petition on your behalf, as long as your current H-1B has been approved for at least 180 days. You don't need to wait for the transfer petition to be approved before starting, which protects you during the transition period.
Does the SOC code assigned to my role affect H-1B approval for Data Operations Analyst positions?
It can. Employers assign a Standard Occupational Classification code on the LCA, and USCIS cross-references it when evaluating specialty occupation. Data operations roles are typically filed under SOC 15-2051 (Data Scientists) or 15-1243 (Database Architects). If the assigned code has a broad educational requirement in the DOL wage data, USCIS may question whether the role truly requires a specific degree field.
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