Acquired
Data Analytics Engineer

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Technology
Midlands
Fully Remote
Data Analytics Engineer
Sometimes the chance comes up to be part of something really special, at Acquired you’ll be collaborating with teams and people who challenge you, support you, and inspire you to be extraordinary.
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).
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.
Department Technology
Locations Midlands
Remote status Fully Remote
Midlands
High Performance and Values driven culture
We have high expectations and work hard to deliver exceptional results.
We value open and honest communication and are committed to listening to our people. Your ideas make us better.
You’ll be collaborating with high performing teams and people who challenge you, support you, and will inspire you to be extraordinary!
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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