Senior Data Engineer

The role

Our tech team is roughly 75% data, 25% software. We are looking for someone to join the data side of our team; turning messy retailer data into something humans and AI agents can trust. In this role, you will own everything from raw source data up to and including the Cube semantic layer that powers the product: modelling, cleaning and serving data in dbt and Snowflake, a healthy amount of data engineering (getting data out of Shopify, PLMs and customer warehouses), and room to grow towards data science if that appeals.

Questions you’ll help us answer:

• Onboarding: How do we take a brand’s raw Shopify, returns and PLM exports and have a trustworthy, queryable model of their business live in days, not months?
• The semantic layer: When a merchandiser reads a dashboard and an agent answers in chat, how do we guarantee they see the same number? What gets defined once, in Cube, and reused everywhere?
• Data for AI: When a buyer asks Maeve “which of next season’s products are most at risk?”, what data needs to exist, and in what shape, for the agent to answer correctly?
• Data quality: Why does this brand’s return rate differ between Maeve and their own reporting, and which is right? How do we catch bad source data before a customer does?
• Cost and scale: Where are we paying for Snowflake queries that could be modelled better? As we go from 25 brands to 100, what has to change so nothing breaks?

Your work will directly shape what major fashion brands see in the product, and what our agents and models can do with the data.

The stack

• Data: Snowflake (including Cortex), dbt, Postgres; ingestion from Shopify, PLMs (e.g. Centric), returns platforms and customer warehouses (e.g. BigQuery)
• Languages: SQL and Python; TypeScript a bonus
• Product: Python API (FastAPI), Cube.dev semantic layer
• Infrastructure: AWS (ECS, Lambda, RDS, S3), Docker, single monorepo
• AI: LLM chat and agents (Anthropic Claude) are core product features; we’re heavy users of AI coding agents internally

What we’re looking for

Must-haves

• 5+ years in analytics or data engineering: Ideally in production environments where your work has directly impacted users or customers
• Strong SQL: From querying and modelling source data to writing ingestion and orchestration code
• dbt and a cloud data warehouse: You’ve designed and owned dbt projects in Snowflake (or BigQuery, Redshift, Databricks), not just contributed models to someone else’s
• Data modelling: You turn inconsistent source systems into a clean, well-documented model that people and AI agents can use without asking you
• End-to-end ownership: You’ve owned production pipelines from source to serving, made the architectural calls and lived with the consequences
• Startup DNA: You’ve either worked at an early-stage company or built and shipped side projects from scratch that people actually use
• Product-minded: You care about the “why” behind a request and can push back constructively when something doesn’t make sense for the business or the customer

Nice-to-haves

• Building ingestion from e-commerce, PLM or ERP systems (Shopify, Centric, etc.), or with orchestration and scheduling on AWS
• Experience with a semantic layer (Cube.dev, LookML, dbt metrics or similar)
• Experience with LLM-powered applications (RAG, agents) or hands-on ML / data science work
• Experience working under GDPR / ISO 27001 requirements
• Understanding of the fashion / retail industry

What matters most to us

• You communicate clearly, to non-technical stakeholders and customers alike
• You balance rigour with speed and take full ownership of getting data into production
• You’re customer obsessed. Enterprise clients depend on the numbers being right, and the best data model is the one that drives a decision
• You’re pragmatic about AI. You use LLMs and coding agents to move faster, and build for them as consumers of your data
• You’re cost-aware by instinct. You notice when a query is burning money and fix it before anyone asks

If you don’t tick every box but this role excites you, please apply anyway; we’d love to hear from you.