Senior Data Engineer

Mesh Connect; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Mesh Connect is seeking a data analytics engineer to own product and client analytics, define core metrics, and maintain centralized data assets using SQL and Python. You will design event schemas and dashboards, and drive data-driven decisions across teams.

You will conduct A/B experiments, validate results against source systems, and build finance-grade reporting with Power BI and Microsoft Fabric for self-serve access.

Qualifications

• Expert in SQL (T-SQL / SQL Server) and Python.
• Hands-on with product analytics (Amplitude, Mixpanel) including event design and funnel/CVR modeling.
• Comfortable with experiment design and statistics for A/B readouts.
• Experienced building data quality checks, reconciliations or backfills on production data.
• In the habit of validating numbers against source systems before anyone else sees them.
• A clear writer who can explain a data issue to an engineer, a CRO and a client in the same day.

Responsibilities

• Own product and client analytics. Define core metrics and maintain central analytics data.
• Run experiment readouts and translate results into product decisions.
• Prove migrations with data and build apples-to-apples comparisons between platforms.
• Guard data integrity with automated reconciliation and verification.
• Shape instrumentation by defining backend event schemas and tracking plans.
• Build finance-grade reporting with parity validation and self-serve access for Finance using Power BI and Microsoft Fabric.
• Make data AI-native by developing tools for internal data agents and audit trails.
• Turn data into client products by shaping self-serve reporting and exports.

Skills

SQL

Python

Product analytics

Experiment design

Statistics

Data quality checks

Data reconciliation

Data storytelling

AI coding tools

End-to-end ownership

Tools

Amplitude

Mixpanel

Microsoft Fabric

Power BI

SQL Server

Spark SQL

Job description

Overview

Mesh moves money across hundreds of exchanges, wallets and chains. The data that flow generates is now a product in its own right: clients want to see their conversion, volume and settlement data, Finance needs reporting they can sign, and our own teams answer most questions by asking an AI agent instead of filing a ticket. You’ll own how we define, instrument, validate and ship that data, working hands-on in SQL and Python and building the guardrails that let AI agents query our data safely.

What You’ll Do

• Own product and client analytics. Define core metrics (conversion rates, transaction success rates) and maintain the central Amplitude data with reusable, templated charts. Build internal and external dashboards for clients.

• Run experiment readouts. Baseline and read out A/B tests on the flow (UI variants, algorithms) and turn results into product decisions.

• Prove migrations with data. Build apples-to-apples comparisons between platforms so decisions move on evidence, not gut feel.

• Guard data integrity. Run and automate daily reconciliation triage and the verification engine.

• Shape instrumentation. Define backend event schemas and the tracking plan, and normalize fields so the data is clean enough for analytics.

• Build finance-grade reporting. Deliver settlement and billing reporting on Microsoft Fabric / Power BI with parity validation, alerting and self-serve access for Finance.

• Make data AI-native. Build skills and tools for our internal data agent, log and audit agent-generated SQL, and keep the self-serve data channels accurate.

• Turn data into client products. Help shape self-serve reporting, reconciliation and billing exports that clients pay for.

Who You Are

• Expert in SQL (T-SQL / SQL Server and Spark SQL a plus) and solid in Python

• Hands-on with product analytics (Amplitude, Mixpanel or similar), including event design and funnel/CVR modeling

• Comfortable with experiment design and statistics for A/B readouts

• Experienced building data quality checks, reconciliations or backfills on production data

• In the habit of validating numbers against source systems before anyone else sees them

• A clear writer who can explain a data issue to an engineer, a CRO and a client in the same day

• A daily user of AI coding and analysis tools

• ~3+ years in analytics or data engineering

• ~5+ years with end-to-end ownership of a data domain.