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Data Analyst / Data Engineer – AI & Analytics

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Long-term contract opportunity € 340 per day Fully remote, anywhere in Ireland, with the opportunity to meet in person in Cork from time to time
Looking for a technically strong Data Analyst / Data Engineer to join a growing technology team on a long-term contract basis.
This is an exciting opportunity for someone who sits comfortably between data analytics, data engineering and AI, and who is passionate about using data to understand, measure and improve what a technical team is building. You’ll work closely with engineering and data science teams to help answer important questions around what data is available, how it should be structured and analysed, and how it can be used to measure outcomes effectively.
Duties:
- Analyse and interpret data to measure the performance and impact of products, systems and technical initiatives.
- Work closely with engineering and data science teams to understand what is being built and determine how it should be measured.
- Build data pipelines and infrastructure to extract, process and analyse data from underlying systems.
- Pull the right data from source systems, move it through your own infrastructure and turn it into meaningful, actionable outputs.
- Use AI tools as part of your day-to-day data analytics workflow to improve productivity, analysis and problem-solving.
- Help define what data is needed, where it comes from and how it should be used.
- Communicate technical findings clearly to both technical and non-technical stakeholders.
- Explain what data is available, what is missing and what is required to answer key business and technical questions.
- Work pragmatically within a cost-conscious environment, finding effective solutions without relying on expensive tooling or large technology budgets.
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.
Requirements:


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- Strong experience in Data Analytics, Data Engineering or a combination of both.
- Strong Python skills.
- Experience working with AWS, Docker, PostgreSQL and AWS S3.
- Experience building or selecting data infrastructure and tools yourself rather than simply operating within a large, fully resourced data environment.
- Experience extracting data from systems, processing it through your own infrastructure and producing meaningful analytical outputs.
- Demonstrable experience of using AI to support or enhance data analytics and data-related work – this is an important part of the role.
- Strong analytical and problem-solving skills.
- Excellent communication skills, with the ability to explain technical concepts and clearly articulate what data is available, what is needed and why.
- A pragmatic, hands-on approach and the ability to work effectively within a cost-conscious environment.
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