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Salary: USD 180,000 - 200,000 / annual
Mesh is building infrastructure to enable consumers to pay and be paid with any asset, bridging the gap between tokenized assets and everyday commerce. The company combines an orchestration engine with a consumer app to unlock liquidity globally, backed by leading investors including SBI, Money Forward, Paradigm, Dragonfly, Galaxy, and PayPal Ventures.
In this role, you will own how Mesh defines, instruments, validates, and ships data across hundreds of exchanges, wallets, and chains. Data has become a product in its own right: clients need conversion, volume, and settlement visibility; Finance requires auditable reporting; and internal teams increasingly query data through AI agents rather than filing tickets.
Key responsibilities include:
- Owning product and client analytics: define core metrics (conversion rates, transaction success rates), maintain central Amplitude data with reusable templated charts, and build internal and external dashboards for clients
- Running experiment readouts: baseline and read A/B tests on UI variants and algorithms, translating results into product decisions
- Proving migrations with data: build apples-to-apples comparisons between platforms to drive evidence-based decisions
- Guarding data integrity: run and automate daily reconciliation triage and verification engines
- Shaping instrumentation: define backend event schemas and tracking plans, normalize fields for clean analytics
- Building finance-grade reporting: deliver settlement and billing reporting on Microsoft Fabric/Power BI with parity validation, alerting, and self-serve access for Finance
- Making data AI-native: build skills and tools for internal data agents, log and audit agent-generated SQL, maintain accuracy of self-serve data channels
- Turning data into client products: help shape self-serve reporting, reconciliation, and billing exports that clients pay for
You will work hands-on in SQL and Python, building guardrails that let AI agents query data safely. The role is based in San Francisco with hybrid expectations (approximately 40% in-office, or two days per week).
Requirements:
- Expert in SQL (T-SQL/SQL Server and Spark SQL a plus) and solid Python skills
- Hands-on experience with product analytics tools (Amplitude, Mixpanel, or similar), including event design and funnel/CVR modeling
- Comfortable with experiment design and statistics for A/B readouts
- Experience building data quality checks, reconciliations, or backfills on production data
- Habit of validating numbers against source systems before sharing
- Clear writing ability to explain data issues to engineers, executives, and clients
- Daily user of AI coding and analysis tools
- Approximately 3+ years in analytics or data engineering
- Approximately 5+ years with end-to-end ownership of a data domain
