Lead Data Engineer

Lead Data Engineer London | Hybrid, 1–3 days per week About Simm er Simmer is one of the UK’s fastest-growing consumer brands, built on a simple belief: healthy food should taste extraordinary. Founded by brothers Simmy and Jhai with just £10 of ingredients, we’ve grown into a profitable, bootstrapped business serving tens of thousands of customers across the UK and ranking as the 5th fastest-growing company in The Sunday Times 100. But we still think we’re early. We’re fully founder-led, with no private equity or corporate layers. The founders still run the business, set the standard and stay deeply involved in the work. That means decisions happen quickly, ownership is real and the people who join us have the opportunity to materially shape what Simmer becomes. Behind that growth is an increasingly complex business. We operate a weekly subscription model, customers can skip, pause, cancel and reactivate, and our data spans everything from acquisition and customer behaviour to revenue, margin, menu and operations. That creates some genuinely interesting data problems. The Role We’re looking for a Lead Data Engineer to take ownership of the foundations our data, reporting and future AI capabilities depend on. This is an engineering role rather than an analytics role. You’ll own the pipelines, models, definitions and infrastructure that sit underneath the numbers the business uses to make decisions. There’s already a meaningful platform in place, built around Microsoft Fabric, a lakehouse and warehouse, dbt and our own BI application. We’ve built quickly, and the next stage is about making that foundation more robust and scalable: simplifying where complexity has built up, strengthening pipelines, creating clearer metric definitions and introducing more engineering discipline around testing, orchestration, documentation and observability. This isn’t a greenfield build. You’ll inherit something real, understand it, improve it and decide what good should look like from here. What You’ll Own Pipelines - build and maintain reliable ingestion from our source systems, with the monitoring and observability to know when something goes wrong. Data modelling - own and develop our dbt layer across areas including revenue, margin, acquisition, LTV, customers, menu and operations. Metric definitions - work with Finance and commercial teams to create clear, consistent definitions for the metrics the business relies on. Data quality - make sure data remains reliable as it moves through increasingly complex customer, commercial and operational journeys. Engineering discipline - improve testing, version control, CI, orchestration, alerting, documentation and lineage. Commercial problems - take ambiguous business questions, understand what actually sits behind them and turn them into robust, repeatable data solutions. Data access - help make trusted, governed data easier for our BI tools, applications and increasingly AI tooling to use. Not every problem needs a platform rebuild. Part of the job is knowing what needs proper engineering, what needs simplifying and when the business simply needs a reliable answer quickly. That balance is important to the original brief, which explicitly combines platform ownership with the ability to turn written business questions into defensible, repeatable answers at pace. Data Engineer_ Job Spec v4 The Problems You’ll Work On Our data is more complicated than a typical ecommerce business. A customer might subscribe, skip a week, change their plan, add additional products, pause, cancel and later reactivate. Transactions and refunds don't always happen at the same point in that journey. So apparently simple questions can become interesting modelling problems. What is an active customer? When has someone actually churned? Which revenue belongs to which period? How should customer value be measured over time? Those definitions matter because they ultimately influence decisions across Finance, Growth and Operations. You'll work closely with the people making those decisions to understand the commercial reality, model it properly and build foundations that remain useful as the business changes. This preserves the genuinely interesting part of Ed's original brief — the interaction between subscription, customer, order and settlement states — without publishing the detailed internal mechanics. Data Engineer_ Job Spec v4 What We’re Looking For We’re looking for an engineer who understands the business problem behind the engineering. You don’t need to have worked in food, DTC or subscriptions before. You do need to have built and run real data systems in production and be comfortable taking significant technical ownership and making good decisions independently. You’ll likely have: Strong SQL and hands-on dbt experience Strong Python for ingestion, transformation and automation Production experience with a cloud data platform such as Fabric, Snowflake, BigQuery, Databricks, Redshift or Synapse Experience building and maintaining data pipelines and API-based ingestion Strong understanding of dimensional modelling Experience with orchestration such as Airflow, Dagster or Fabric pipelines Git, code review, testing and CI as normal engineering practice Experience thinking seriously about data quality, reliability and observability Microsoft Fabric experience is useful, but it isn’t essential. The underlying technical requirements are much broader than one particular platform. Data Engineer_ Job Spec v4 More importantly, you: can take an ambiguous commercial problem and turn it into a clear technical solution want to understand what a metric actually means before you model it can explain technical decisions clearly to non-technical people are comfortable challenging a definition or approach when you think there’s a better answer can inherit something imperfect and improve it rather than needing to rebuild everything care about making systems simple, reliable and maintainable know when to engineer for scale and when to keep the solution simple are comfortable making technical decisions independently We care more about the depth of problems you’ve owned than the size of the engineering team you’ve worked in. The Stack Our data platform is built around: Microsoft Fabric → Lakehouse → dbt → Warehouse → our own BI application It brings together data from our custom commerce platform and the different systems used across the business. Our commerce model is deliberately custom because a weekly meal subscription behaves differently from traditional ecommerce. That means there’s meaningful engineering work here rather than simply connecting a standard commerce platform to an off-the-shelf reporting tool. Why Simmer? This is an opportunity to take meaningful technical ownership inside a fast-growing consumer business. You won’t be one of a large team of data engineers responsible for a small part of the stack. You’ll have real influence over how data enters the business, how it’s modelled, how important metrics are defined and how the platform develops from here. There’s already enough infrastructure and complexity for the work to be interesting, but still plenty to improve and build. And because the team is lean, you’ll work directly with the people using what you build across Finance, Growth, Operations and the wider commercial team. You’ll see the connection between an engineering decision you make and how the business operates. If you’re a strong data engineer who wants broader ownership, interesting commercial problems and the opportunity to build something properly, we’d like to hear from you.