Founding Data Engineer

Imagine AI (YC F25); San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Imagine AI is building a data-powered product experience. We are seeking a data engineer who will own the data pipeline end to end, from ingestion to dashboards, and who can translate customer feedback into measurable product improvements.

You will work with a small team to ship customer-facing analytics, leveraging SQL, Python/TS, and a Next.js-based stack, delivering insights directly in our product.

Qualifications

• 2–5 years of experience in data or analytics roles.
• Built and operated end-to-end production data flows.
• Shipped customer-facing dashboards or analytics inside a product.
• Proficient in SQL and TypeScript or Python; able to ship with Next.js.
• Applied simple models (classification, scoring, clustering) in production.

Responsibilities

• Own data pipeline end-to-end: scraping, ingestion, storage, cleaning, serving.
• Build metrics and dashboards; convert data into customer insights.
• Engage with customers on calls; translate feedback into product updates.
• Collaborate on the product data stack; implement incremental improvements.

Skills

SQL

TypeScript

Python

Next.js

Data pipeline

Dashboard development

ML basics

Tools

PostgreSQL

MongoDB

Neo4j

Job description

About Imagine AI

We encourage you to read more about how we operate in our handbook.

Our mission is to model B2B growth and build products that accelerate it.

We reverse engineer B2B growth, starting with LinkedIn. We turn a company’s executive team into a coordinated content engine: content across the whole leadership team, engagers resolved to named accounts and roles, and engagement written back to the CRM. That means content spend stops being a branding line item and becomes provable pipeline.

We serve over 30 high growth B2B companies from Series A to IPO’d, including MongoDB, Rippling, and Corgi Insurance. We started in June 2025 and launched out of Y Combinator’s F25 cohort. Our co-founders are Sky and Neo.

Tech stack

Nextjs, Typescript, Supabase (Postgres), MongoDB, Neo4j, trigger.dev, Apify, Vercel, Git

Our data stack is intentionally scrappy: scheduled trigger.dev jobs that scrape (via Apify), clean, and transform in-run. It works at our scale. You will evolve it as we grow, not replace it.

What your day will look like

You will be one of our first few hires at Imagine AI, which means you will have a huge responsibility and impact in our business. You will own our data pipeline end to end: scraping and ingestion, storing, cleaning, metrics, simple models, and dashboards in the product. Concretely, that means building metrics, monitoring content performance, and synthesizing marketing and network data into insights our customers see directly in the product.

You will join customer calls, talk to them directly, and translate what you learn into the metrics and insights customers see immediately. You will also live on slack and provide customer support directly to our customers. This means if you are excited about owning the entire lifecycle of a data product, you should come work with us! You will see your effort being reflected in our revenue curve directly!

Hard requirements

• 2-5 years of experience
• You must have built and operated production data flows end to end (collection/scraping, storage, cleaning, serving) at any scale
• You must have shipped customer-facing dashboards or analytics inside a product, not only internal BI
• You must be proficient in SQL and TypeScript or Python, and able to ship the last mile in our web stack (Nextjs)
• You must have applied simple models in production (classification, scoring, clustering) and know when not to use ML

Soft requirements

• You must love talking to customers and translating their feedback to product updates
• You must write clean and maintainable code
• You must have taste for which metrics matter and judgment for right-sized architecture: you build on our existing stack and change it incrementally when there is real pain, not to chase best practices

Compensation

Read more at the compensation section in our handbook.