Who we are

Copilot Money is on a mission to help people stress less about money. We’re building a new kind of financial platform that empowers people to take control of their finances, make informed decisions, and achieve their goals. We're a high-energy, fast-paced startup looking for determined, hands-on folks to join our team and make a real impact on people's financial lives.

The Role

In this role, you'll be part of our Engineering team, working cross-functionally with Marketing and Finance.

This is a hybrid position based in New York City you’ll be required to come into our office at least 2 times a week along with the rest of the team. We work from home the rest of the week, but the office is always available in case you want to make use of it more often.

Key Responsibilities

Understand the data needs of finance, marketing, and product teams in terms of key data models and centralized reporting, and translate that into technical requirements.

Define, build and manage key data pipelines, foundational data products, dashboards and tools to enable self-serve analytics to scale across the company.

Become an expert in Copilot Money’s data models and the company's data architecture.

Own attribution and conversion measurement end-to-end: server-side tagging (sGTM), conversion APIs, and audience sync across paid channels (Meta, Google, etc.) and payment providers (Apple, Stripe).

Own analytics instrumentation across web, iOS, and the marketing site (GA4, Amplitude): event taxonomy, tracking setup, event audits, and closing data gaps so product, marketing, and finance can trust the numbers.

Maintain marketing integrations (sGTM, Customer.io, Impact, AppsFlyer, etc.) and stand-up new integrations.

Support consent and compliance tooling (Transcend, Google Consent Mode, cookie consent) to maintain privacy-safe standards as regulations evolve.

Manage marketing website operations: landing pages, tracking setup, troubleshooting, and technical support for SEO/AEO/GEO work.

Explore and build automation systems that improve brand visibility across search and AI-generated answers (i.e., designing content architecture and schema frameworks to increase citation frequency in LLMs.)

Required Qualifications

3+ years of experience in analytics engineering, marketing engineering, data engineering, or a similar technical role with a marketing-facing component.

Expertise in SQL and JavaScript/Node to transform data into accurate, clean data models.

Hands-on experience with GA4, Google Tag Manager (including server-side), and BigQuery, implementing them, not just reporting.

Experience with attribution and conversion tracking for paid channels (conversion APIs, audience sync, event deduplication).

Familiarity with marketing and analytics tooling (i.e., Amplitude, Customer.io, Impact, AppsFlyer, etc.) or similar.

Strong communicator who can work across engineering, marketing, and finance, translating between them as a core part of the job.

Team player who can manage time independently.

Based in NYC.

Desired Qualifications

Bachelor’s degree in Computer Science or equivalent.

Swift

Familiarity with cloud data environments (AWS, GCP, or Azure) and columnar/analytical databases.

Experience working with AI agents, LLM-powered tooling, or similar systems.

Why should you work with us?

A product people love: Copilot users are extremely engaged with the app and are sharing it with their friends and family.

A team that cares: We’re a bunch of geeks who care about data quality and good coffee. We go the extra mile when it comes to making our users happy (and making an excellent pour-over).

Significant impact: We are currently a small, thirty-five-person team with big ambitions. Our team has previous experience at companies like Google and Apple.

Multicultural team: We’re based in NYC 🇺🇸 and in Santiago, Chile 🇨🇱.

Great company culture: You’ll work with a highly collaborative team that’s always learning and ready to help each other out.

Compensation and Benefits

The expected salary range for this role is $155 - $185k. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity.