Acquired
Data Analytics Engineer
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We’re Acquired
Recurring Payments. Redefined.
Acquired helps businesses win and retain customers. We process payments intelligently and optimise every aspect of the recurring payment lifecycle.
We combine this capability with exceptional sector expertise and a highly personal, tailored service focused on long-term partnerships with our customers.
How we work matters as much as what we build. We’re hugely ambitious and passionate about recurring payments. As a team, we pride ourselves on being relentlessly focused on results.
If that's how you operate, you could be a great fit for Acquired.
Your Mission
Acquired's commercial, product, finance, and payments teams increasingly run on data. The Data & Analytics team turns warehouse data into the metrics, models, and analysis those teams use to make decisions.
This is a Data Analytics Engineering role — modelling, semantic layer, and stakeholder-facing analysis — not data infrastructure. Pipeline orchestration, ingestion, and platform work is owned elsewhere on the team.
We're open on level. The role is a fit for:
- An early-career engineer or analyst keen to learn modern analytics engineering on the job.
- A strong data analyst ready to step into modelling, dbt, and end-to-end ownership.
- An established analytics engineer who wants a broad, high-agency remit across a payments business.
What the role involves
The day-to-day mix sits across:
- Modelling — building and maintaining the warehouse models (facts, dimensions, marts) that downstream analysis depends on.
- Semantic layer & metrics — defining the governed metrics business teams use (revenue, margin, conversion, retention, pipeline).
- Stakeholder analysis — partnering with Commercial, Product, Finance, and Engineering to scope questions, deliver answers, and run UAT.
- Dashboarding & self-service — Data Studio dashboards and tooling that lets non-analysts answer their own questions.
- Data documentation & democratisation — keeping the warehouse documented and approachable, including our AI data assistant (Dot).
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How we work with Claude Code
We use Claude Code heavily across the team — for dbt model authoring, ticket implementation, documentation maintenance, code review, and analytical exploration. You'd be expected to be comfortable working alongside it from day one, or get comfortable quickly. We have a written operating model for how engineers and agents work together; we'll share it with you during the process.
What that looks like in practice:
- Humans stay accountable. The agent does work; you own the result. Human attention is spread across the workflow — intent, approach, in-flight, sign-off — rather than concentrated at PR review.
- We extend the tooling. Custom skills tailored to our stack, MCP integrations across the tools we use day-to-day, slash commands and hooks for repetitive workflows. Curating these is engineering work — you'll use what's there, refine what isn't working, and propose new things.
- Evidence-first investigation — we expect both humans and AI agents to cite file/line, query output, or commit history before making claims. "Trust but verify" applies to everything an assistant produces.
- The team's bias is: let AI do the rote work, spend human time on judgement, design, stakeholder partnership, and the bits where context matters most. If that resonates, you'll fit in well. If it sounds like a distraction from "real" analytics work, this probably isn't the right team.
What You'll Bring
We do not expect you to have prior experience with every tool in our stack. SQL is our only hard-and-fast technical requirement. For the rest, we're looking for strong analytical fundamentals and a high capacity and eagerness to learn.
Must-haves
- SQL — fluent or rapidly improving. Window functions, CTEs, performance-sensitive query design. We use BigQuery.
- Analytical thinking — ability to break down ambiguous problems, interpret data, and translate findings into decisions.
- Self-direction — comfortable owning work and asking for help when stuck. Your manager is an active individual contributor, so expect low control and high agency.
- Communication & stakeholder posture — comfortable proactively setting up meetings, interviewing stakeholders, and writing things down. Visiting Nottingham/London to build relationships in person.
- Eagerness to learn — excited about picking up new tools (dbt, the Semantic Layer, Dagster, Data Studio, BigQuery ML) on the job.
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Nice-to-haves
If you have experience with some of these, that's great. If not, you should be excited to learn them:
- Dimensional modelling — facts, dimensions, grain, how to extend a model without breaking downstream consumers.
- dbt — staging/intermediate/mart layers, tests, snapshots, macros, incremental models.
- Semantic layers (dbt Semantic Layer ideally; Cube.dev or LookML transferable).
- Data Studio or another BI tool.
- Payments or fintech domain (cards, acquiring, IC++ pricing, routing) — domain training is fine if you bring strong fundamentals.
- Working alongside AI coding assistants (Claude Code, Cursor, Copilot) — comfortable delegating, reviewing, and steering AI-generated work.
About Us
A two-person Data & Analytics team inside Acquired's wider Engineering function.
A modern warehouse stack: BigQuery, dbt (Core/Semantic Layer, moving toward Fusion), Dataflow (Beam) for extracts, Data Studio for dashboards, Dagster (deploying mid-year) for orchestration.
A genuinely cross-functional remit — your stakeholders span Commercial, Product, Finance, and Engineering.
An organisation in the middle of a platform rebuild, an invoicing-platform replacement, and a Finance Transformation programme — plenty of meaningful greenfield work.
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