HM Revenue & Customs
Data Engineer

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Data Engineer - Digital Representation
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.
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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.
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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.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
We are seeking an experienced Data Engineer to design, build and maintain scalable data solutions that support the Digital Representation (DR) platform. You will develop and optimise data pipelines, integrate data from multiple source systems and deliver trusted, high-quality datasets that underpin forecasting, simulation and reporting capabilities.


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Essential Skills & Experience
- Experience building and maintaining ETL/ELT pipelines and data integration solutions across multiple source systems.
- Strong AWS data engineering experience, including cloud-native data services, data cataloguing, metadata management and data transformation tools.
- Proficiency in SQL and Python for data processing, automation and optimisation.
- Experience developing data wrangling and transformation processes, including data cleansing, standardisation, deduplication and preparation of model-ready datasets.
- Knowledge of data lineage, data quality controls, governance and auditability principles.
- Experience managing cloud-based data storage, data lakes and data warehouse environments.
- Understanding of DataOps, DevSecOps and CI/CD practices, including automated testing, deployment and monitoring.
- Ability to work with technical and business stakeholders to deliver scalable, reliable and reusable data solutions.
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