Graduate Data Engineer

OCU
Preston
1 month ago
Applications closed

Related Jobs

View all jobs

Graduate Data Engineer

Graduate Data Engineer - Hybrid, Insights & Automation

Graduate Data Engineer

Graduate Data Engineer

Graduate Data Engineer

Graduate Data Engineer: Azure Data Platform Explorer

Graduate Data Engineer
Role Overview

The Graduate Data Engineer will join an established team of Data Engineers Data Analysts and Data Scientists developing for the OCU Data Platform. They will utilise modern Data Engineering techniques building robust data pipelines to ingest data of various formats into the Data Platform as well as transforming data extracting data and providing key insights to the business.


Duties and Responsibilities

  1. Data Engineering and Architecture: Assist in building and maintaining robust data architectures pipelines and systems tailored to support decision-making in the utilities construction industry and related sectors.
  2. Data Integration and Management: Facilitate efficient data integration and management from multiple sources within the Group ensuring data accuracy and consistency.
  3. Data Processing and Automation: Aid in developing automated processes for data extraction transformation and loading (ETL) to streamline data workflows.
  4. Industry Awareness: Maintain awareness of industry developments particularly in innovative areas like Utilities 2.0 and incorporate this knowledge into data engineering practices.
  5. Collaboration and Teamwork: Collaborate with different teams within the Group addressing their data engineering needs and contributing to tailored solutions.
  6. Off-the-Job Training: Engage in comprehensive off-the-job training that includes theoretical instruction practical training and industry exposure.
  7. Graduate Programme Participation: Actively partake in the Graduate Programme blending hands‑on experience with formal training as per statutory requirements.
  8. Cross-functional Support: Offer support across various departments contributing to diverse stages of project development and execution.
  9. Development Standards: Following established OCU Data Team development standards ensuring that all completed work is correctly source controlled.

Qualifications and Skills
Desirable

  • Knowledge of programming concepts and principles
  • A genuine interest in data engineering and a commitment to ongoing learning in the field.
  • Strong problem-solving abilities and a systematic approach to technical challenges.
  • A keen eye for detail ensuring accuracy in all aspects of data handling.
  • Effective communication skills facilitating collaboration and technical knowledge sharing.
  • A team player mindset contributing to and benefiting from collaborative efforts
  • Knowledge of source control tools (such as Git)
  • Familiarity with cloud-based data platforms and tools such as Microsoft Azure Databricks Apache Spark or related technologies with an interest in developing practical skills in modern scalable data processing environments.

What We Value

We value our commitment to each other summed up in our five values we all sign up to these We care about safety. We lead with integrity. We strive to be better every day. We make a positive impact. We deliver to grow. We are one company united.


Our Aim & Vision at OCU

To be the UKs leading energy transition and utilities contractor.


We are committed to leading the way in utilities and energy transition contracting our mission is to innovate and deliver sustainability. At OCU our passion for addressing complex challenges brings new standards of growth in our people and capabilities. OCU is an equal opportunities employer.


Key Skills

Clerical, Engineering, Assembly Language, Credit, Clinical


Employment Type

Full Time


Experience

years


Vacancy

1


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.