FDJ UNITED
Senior Data Engineer

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A hands-on engineering role building and optimising scalable ETL/ELT pipelines and Lakehouse architectures across a modern multi-cloud platform using Python, Spark, Airflow, and dbt.
Requires 5+ years of data engineering experience with strong expertise in SQL, CI/CD, data quality frameworks, and cloud technologies (S3, Athena, Redshift), with a plus for streaming and AI tooling experience.
Overview
A Senior Data Engineer position within an agile team, focused on building scalable data pipelines and modernizing a multi-cloud data platform. The role involves working with streaming technologies, Lakehouse architectures, and leveraging automation/AI to improve efficiency.
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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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.
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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.
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.
What you will do
- Pipeline Development: Build ETL/ELT pipelines (Python, SQL), optimize Spark/PySpark jobs, manage Airflow DAGs, and implement dbt transformations.
- Lakehouse Architecture: Design medallion-style architectures using S3 and modern table formats (Iceberg, Delta Lake) with optimized layouts.
- Platform Infrastructure: Create reusable frameworks, enforce data contracts, build CI/CD pipelines, and establish observability practices.
- Data Quality: Implement validation frameworks, data contracts, and SLIs/SLOs for critical pipelines.


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Your experience
Required
- 5+ years in data engineering
- Strong Python, advanced SQL, Airflow, Spark/PySpark, dbt, and CI/CD proficiency
- Experience with Lakehouse architectures, dimensional modeling, and cloud data warehouses (S3, Athena, Redshift)
- Data quality frameworks, monitoring, and schema evolution expertise
Nice-to-Haves
- Streaming platforms (Kafka, Kinesis)
- AI coding assistants (Cursor, Claude Code, Copilot)
- Terraform, Kubernetes/Docker
- Data catalog/metadata management experience
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