Analytics Engineer, Strategic Finance
whop · Palo Alto, CA ·
- Work mode
- Onsite
- Category
- Data analytics
- Experience
- 3+ years
whop · Palo Alto, CA ·
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Our mission is to deliver everyone a sustainable income. Right now 50,000 users make money on Whop every month, and this year users will earn over $5B. We will not stop until 1B people every month are making money on Whop.
We're hiring our first dedicated Analytics Engineer inside Strategic Finance, and the job is simple to state and hard to do well: make everyone at Whop smarter with the insights hidden in our data, and change how the company makes decisions.
Whop generates an enormous amount of data — every payment, dispute, refund and ad campaign tells us something about the business. Most of it isn't yet answering directly the questions we are actually asking. You will build the datasets, models, and metrics that turn that raw signal into fast, trustworthy answers — and then push those answers in front of the people making the calls, so that a good decision is the path of least resistance.
You'll write production code and ship changes directly to Whop's financial products. You'll build and own the data pipelines that ingest payment-processor settlement data into that ledger, encode accounting logic in code (bad-debt recognition, FX gain/loss, revenue categorization), and make the reported numbers reconcile to source.
This role is on-site in Palo Alto.
Turn data into decisions. Find the insights that change what Whop does — surface trends, risks, and opportunities in payments, growth, and unit economics, and get them in front of the right people in a form they can act on immediately.
Ship Whop financial product. Write production code and build features on Whop Financial Reports so it's reliable enough to close the books on and useful to every merchant.
Build and own the ingestion pipelines. Ingest and backfill payment-processor settlement data into the ledger, at scale and across currencies and geographies, so reported financials are complete and accurate always.
Own the financial semantic layer. Define and standardize metric logic (GMV/GTV, revenue, take rate, contribution margin, COGS/GP per business, LTV/CAC, retention/cohorts, chargeback and dispute rates) so one metric means one thing across the organization.
Make finance and the business self-serve. Partner with leadership to ship intuitive, trustworthy Metabase dashboards and data models so every stakeholder can answer their own questions with confidence — and stop pinging you for a number.
Set standards. Establish data modeling conventions, testing, documentation, and access controls, and own data quality and lineage across the stack.
3+ years in Data Engineering, Analytics Engineering, or a closely related role, ideally in a high-growth or fintech environment.
Deep expertise in SQL and Python, plus a modern transformation and warehouse stack (dbt + BigQuery preferred; Snowflake/Redshift equivalent fine).
Proven experience defining financial and operating metrics and resolving data inconsistencies across complex, multi-system pipelines.
Strong grounding in version control (GitHub), CI/CD, testing, and documentation as defaults, not afterthoughts.
Working knowledge of financial concepts: revenue, margin, LTV/CAC, retention/churn, and the difference between an accounting number and a product metric.
A strong communicator comfortable with ambiguity, building trust across Finance, Product, GTM and Exec partners.
Competitive base salary, meaningful equity, and full benefits.
Target base range: $200,000-$300,000
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