Founding Data Engineer

Socket.dev; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Imagine AI is looking for a data product engineer to own the data pipeline end-to-end, from scraping and ingestion through storage, cleaning and dashboards in the product. You will build metrics, monitor content performance, and synthesize data into insights used by customers.

You will join as one of the first hires, participate in customer calls, and translate feedback into product updates. You will work on the stack including SQL, TypeScript, Python, and Next.js, shaping how data drives

Qualifications

• 2–5 years of experience in data/engineering or a similar role.
• Built and operated production data flows end to end at any scale.
• Shipped customer-facing dashboards or analytics inside a product.
• Proficient in SQL and TypeScript or Python; able to ship in Next.js.
• Applied simple models in production (classification, scoring, clustering) and know when not to use ML.

Responsibilities

• Own the data pipeline end to end from scraping/ingestion to dashboards.
• Build metrics, monitor content performance, and derive insights for customers.
• Engage with customers on calls and translate feedback into product insights.
• Collaborate closely with frontend and product to ship features on the data tier.

Skills

Data pipeline ownership

End-to-end data flows

Customer-facing dashboards

SQL

TypeScript

Python

Next.js

ML basics

Job description

About Imagine AI

We automate LinkedIn marketing through executive led content for mid market companies. We are serving over 30 companies from Series A to IPO’d companies, including MongoDB, Rippling, and Corgi Insurance. We started in June 2025 and [launched out of Y Combinator’s F25 cohort](https://www.ycombinator.com/companies/imagine-ai). Our co-founders are [Sky](https://www.linkedin.com/in/skyyang/) and [Neo](https://www.linkedin.com/in/neo-lky/).

Tech stack

Nextjs, Typescript, Supabase (Postgres), MongoDB, 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](https://www.youtube.com/watch?v=MT4Ig2uqjTc), 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](https://docs.imagineai.me/#2eda6fc9fa6880e2b6f7e919bedb7de9).