Jobs / United States / Cricut INC
Analytics Engineer
Cricut INC · 🇺🇸 South Jordan, UT, US
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 20 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 2 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Cricut INC paid sponsored hires in similar roles2 certified filings for “Manager, Test Development Engineer” (Architectural and Engineering Managers) in UT: $147k–$147k, median $147k. Most were filed at wage level II (100%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Cricut INC
The US Department of Labor certified 20 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 2 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Cricut INC →
About the role
The Analytics Engineer combines data engineering and data analysis to provide dedicated analytics support for the Customer Care organization while reporting to the Corporate Business Intelligence team. This role manages the full lifecycle of Customer Care data—from sourcing, integrating, and transforming data across multiple support systems into reliable analytical datasets, to analyzing that data and delivering actionable insights that support operational and business decisions. Working closely with Customer Care leaders and the Business Intelligence team, this role builds and maintains the data foundations, reporting, dashboards, and analytical solutions needed to improve Contact Center performance and customer experience. The Analytics Engineer proactively identifies data and reporting needs, recommends analytical solutions, and drives continuous improvement through scalable, reliable, and actionable data. Key Responsibilities Data Engineering & Data Management • Design, build, and maintain analytical tables and datasets used for reporting, dashboards, and forecasting. • Ingest and transform raw data from contact center systems (e.g., ACD, CRM, ticketing, chat, email) into clean, structured datasets. • Ensure data accuracy, consistency, and integrity across multiple source systems. • Optimize table structures and queries for performance and scalability. • Document data definitions, transformation logic, and assumptions to support transparency and reuse. • Develop and apply AI models to analyze customer contact transcripts, surveys, and other feedback sources, identifying key trends, themes, and actionable insights that inform business and customer experience decisions. End-to-End Contact Center Data & Analytics • Own the end-to-end analytics workflow for Contact Center data: • source systems → ELT to Datalake -> dim/fact table -> transformed analytical tables → reports/dashboards → insights. • Work with data from multiple Contact center platforms, including ACD, CRM, ticketing systems, chat, and other digital interaction tools. • Translate reporting and analytics needs into scalable data models and analytical outputs. • Ensure that insights are built on consistent, well-defined, and trusted data sources. Contact Center Performance Analysis & Insights • Analyze Contact Center performance across key operational metrics, including service level, ASA, AHT, contact volume, abandonment, occupancy, shrinkage, and adherence. • Develop and maintain recurring and ad-hoc reports and dashboards for operational teams and leadership. • Identify trends, risks, and opportunities impacting efficiency and service performance. • Perform root cause analysis to explain performance gaps and operational challenges. • Measure the impact of operational initiatives using before-and-after analysis and ongoing performance tracking. Customer Behavior & Experience Analytics • Analyze customer interaction data across channels to understand contact drivers, behavior patterns, and end-to-end journeys. • Track and interpret customer experience metrics such as CSAT, NPS, FCR, repeat contacts, and transfers. • Identify drivers of customer dissatisfaction, high contact volume, long handle times, and repeat contacts. • Translate analytical findings into insights that support improvements in self-service, agent workflows, and process design. • Build dashboards and analyses that connect customer behavior to operational and business outcomes. Forecasting & Volume Modeling • Develop, maintain, and refine forecasting models to predict contact volume by channel and contact reason. • Validate forecasts against actual results and identify drivers of variance. • Continuously improve forecast accuracy using historic