Harper
Data Analyst
San Francisco · Mid
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
$2,438 more — $142,438 — moves this role to Level II and 2 lottery entries. That figure is inside the range the employer already advertised.
Based on the DOL prevailing wage for this occupation and worksite, a base salary of $142,438 would place this position at wage Level II. That figure is within the posted range, and I'd like to target it. This role classifies under "Data Scientists" for prevailing-wage purposes.
DOL prevailing wage, 2026-27 wage year · Data Scientists (15-2051) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- You're the one who tells us what it's saying.
- Every morning you'll wake up asking the same question:
- You know what good channel performance looks like, you understand the metrics that matter in paid acquisition, and you can translate a cohort analysis into a budget recommendation without anyone having to ask.
Responsibilities
- Synthesize channel performance. Pull together data across paid, organic, and partner channels - and identify what's working, what's not, and what to do about it.
- Partner with the Growth Marketing Lead. Serve as their analytical right hand
- Own growth KPI reporting. Define, maintain, and communicate core marketing and funnel metrics so the team is always looking at the right numbers.
- Build and maintain LTV/CAC analysis. Give leadership a clear, current picture of unit economics by channel, cohort, and customer segment.
- You've worked as an analytical partner to a marketing or growth team - not just supported them from a distance.
- Monday-Friday, long days, in the building with the team.
- Proven ability to communicate analytical findings clearly to non-technical stakeholders and drive decisions
Requirements
- 4+ years of analytics experience with direct exposure to marketing, growth, or revenue teams
- Hands-on experience with funnel analysis, cohort analysis, attribution, and LTV/CAC modeling
- Experience analyzing paid acquisition channel performance (Google, Meta, or comparable)
- Experience partnering with a marketing or growth counterpart to act on data in real time - not just produce reports
- You can draw a clear conclusion from messy data and defend it in a room with people who will push back.
- You're fast: you can turn a question into an answer in hours, not days.
Nice to have
- Familiarity with PostHog, Mixpanel, Amplitude, or comparable product analytics platforms
- Experience working alongside a data engineer or GTM engineer and understanding how data pipelines work
- Background in insurance, fintech, or another industry with complex funnel and attribution dynamics
- Exposure to AI-assisted analytics workflows and modern BI tooling
- Experience in a high-growth startup environment where speed and adaptability were required
- People screen - fit and alignment
- Super day - how you operate in real time
- Send your resume and an example of analysis you ran - what you recommended, and what the team did with it.
Compensation
- $140,000-$180,000, plus performance bonus and equity.
Benefits
- Uber commuter benefits
- Free gym membership
- Health, dental, and vision insurance
This listing is sourced directly from Harper's careers page and normalized into a canonical job model.