Founding Data Engineer

We are Harper, a fast-growing Series A startup at the intersection of eCommerce and Fintech.

Harper is pioneering the next generation of online shopping. We bring the fitting room experience directly to customers at home, giving them the freedom to touch, feel and try on items before they buy. No upfront payment. No credit. Just the confidence to shop the way you always could in store, from wherever you are.

The result is a shopping experience that turns hesitant browsers into confident shoppers, and a commercial proposition retailers can measure. Stronger conversion, customers who come back, and growth that comes from serving people better.

We work with some of the world's leading fashion brands - Paul Smith, ME+EM, Victoria Beckham, Mint Velvet, Universal Works and many more. We're building the infrastructure for a new era of online retail.

Role overview

We're hiring our Founding Data Engineer to own Harper's data end-to-end.

Harper runs on data. Brand performance, customer behaviour, returns, payments, partner integrations - every commercial decision we make traces back to whether the numbers are right. Right now, the volume and complexity has outgrown the patchwork keeping it together. We need someone to build the foundation properly.

This is a founding role. You'll own Harper's data and set the bar for what good data looks like ; designing the architecture, connecting the sources, and making sure the entire business can query, trust, and act on it. We've recently introduced an AI-assisted querying layer that gives the whole business direct access to our data. Your job is to make sure what it returns is accurate, well-documented, and trustworthy. Every dashboard, every brand review, every product decision gets better when the foundation is right.

What you'll own

The infrastructure - Design, build and own Harper's data lake and pipeline infrastructure end-to-end. Connect new data sources as the business scales. Build and maintain a single source of truth that the whole business can query and rely on.

Data quality - Define testing standards, validation logic and observability that catches problems before they reach stakeholders. Own the data dictionary - metric definitions and table-level context written clearly for both humans and AI agents.

The business relationship - Work directly with non-technical colleagues to translate their questions into reliable pipelines and well-defined metrics. Collaborate with engineering on product features where data is part of the design. Bring a product mindset - curious about what a metric is meant to drive, not just how it's calculated.

About you

You've had hands-on ownership of data infrastructure in production and you know how to design data models for a modern warehouse. You have strong SQL skills and confidence with pipeline orchestration - whether that's Airflow, AWS Glue, dbt or something similar. You have a sharp instinct for data quality, and you write clearly enough to explain complex concepts to a non-technical colleague without losing the nuance.

Most importantly, you're excited to build from scratch. You'd rather set the standard than inherit someone else's.

Our stack, for context: RDS Postgres, MongoDB, AWS Athena, Parquet, AWS Glue, Airflow, Python, Docker, S3, GraphQL, REST. You won't know all of it, you'll be strong in your core area and curious about the rest.

Nice to have : Python proficiency, CI/CD for data workflows, graph database experience (Neo4j), startup or early-stage background.

What we offer

• Competitive salary + meaningful share options at a stage where they can grow significantly

• Real ownership and autonomy from Day 1, working directly with the founders

• 32 days holiday (take public holidays whenever you like) with a 3-day carryover policy

• £600 annual wellbeing allowance

• MacBook and the tools you need to do your best work

• Hybrid working and regular team socials

• Based at Highgate Studios - one of London's best creative campuses, with a café, bike storage, showers and great social spaces. A short walk from Kentish Town Underground and Gospel Oak Overground

Interview process

1. 20-minute intro video call – get to know each other, hear more about Harper

2. 30-minute technical video call – your skills and approach to problem-solving

3. 90-minute in-person interview – a real Harper data scenario, worked through together

4. Offer