Analytics Engineer

The Role

This is a dual-track role: roughly half your time will be spent on data engineering (pipelines, dbt models, CI/CD, data quality) and the other half on analytics and modelling (exploring data, quantifying value, supporting product and sales). You will be the second person on the data team, working closely with the Head of Data.

This is not a narrow, ticket-driven role. We are a six-person company and we need people who roll their sleeves up. If something is critical to the business, we all work on it.

In your first 12 months, your key priorities will include:

• Consumer identity & matching: building and proving out credit reference agency-style consumer identification and matching pipelines. This needs to work, and it needs to be demonstrably excellent.
• Bringing our quarterly model in-house: we currently rely on an externally owned statistical model that runs each quarter. You'll help us own and operate this ourselves.
• Data quality & monitoring: maturing how we observe and monitor data from source through to output. You'll help us catch issues before clients do.
• Product development: all of our products are data products. You'll work alongside the Head of Data to identify and build new features and data products.
• Sales support & proof of concepts: supporting the commercial team with POCs and POVs, with a view to automating the most repeatable ones.
• AI adoption: the data team leads AI usage across the business. You'll be hands‑on with Claude and Gemini, finding ways to embed AI into our workflows and products.

You don't need to know all of this on day one, but you should be comfortable with the core and curious about the rest.

• Cloud - AWS
• Data warehouse - Snowflake
• Transformation - dbt (with CI/CD via Git Hub Actions)
• Operational DB - MongoDB
• AI / LLM - Claude (Anthropic), Gemini (Google)
• Version control - Git Hub

• Strong SQL: you think in SQL and write clean, well‑structured queries as second nature
• Solid dbt experience: you understand models, tests, sources, and how to build maintainable pipelines, or are interested in learning quickly here.
• Consumer data background
• Data quality mindset: you have built or contributed to monitoring, alerting, or observability on data pipelines
• 2–4 years of hands‑on experience in a data engineering or analytics engineering role

• Experience with Snowflake
• Python for data modelling or ML; even if self‑taught
• Familiarity with AWS (S3, IAM, basic infra)
• Any experience using LLMs or building AI‑assisted tooling
• Supporting sales or non‑technical stakeholders with data work

Skills get you through the door. This is what makes you right for us:

• You're comfortable being the person who figures things out; we're small and there's no large team to hand off to
• You care about doing things properly: clean code, documented pipelines, data you'd stand behind
• You're genuinely curious about data and want to understand what it means, not just move it
• You're happy to work on whatever the business needs, even if it's outside your comfort zone
• You want to be part of something early and help shape how it grows

• Real ownership; you'll have meaningful responsibility from day one, not a queue of tickets
• A small, high‑trust team where your work is visible and valued
• The opportunity to help build a data team and data function from close to the ground up
• Hands‑on experience with modern AI tooling as a first‑class part of the job
• Hybrid working with flexibility built in

We are a small, ambitious data technology company. Our entire business is data: we buy, enrich, and sell data on personal finance solutions, delivered to clients via API or data extract. We are a team of six, which means every person has real impact and genuine ownership of their domain.

The data team sits at the core of the business: we own the Snowflake environment, build and maintain production data pipelines in dbt, and are responsible for the quality, integrity, and commercial value of everything we produce. Our Engineers handle ingestion (S3 → Snowflake) and write‑back (Snowflake → S3 → Mongo

DB); the data team owns everything in between and…