Data Engineer - Junior

Binley Woods
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We’re building thriving communities as one of the UK’s largest housing groups and a leading developer of affordable housing.

We believe everyone is entitled to a good home they can afford, in a place they are proud to live. More than 100,000 people live in our homes.

If you want to experience work that’s truly rewarding, join us. Because when we achieve together, customers and communities thrive.

Work for Orbit. Believe in people.

The role

Orbit are delighted to announce that we are recruiting for a Junior Data Engineer. In this role you will be responsible for the practical delivery of end-to-end Data Engineering activities utilising Azure technologies to import data to a central data warehouse where it is modelled to create business ready InfoMarts for consumption in PowerBI.

You will also work as part of the Data Engineering team to embed best practice for the design, implementation, delivery and support numerous complex dataflows to connect operational systems.

This will be an agile working role, which will require you to travel into our offices to work onsite roughly one day per week (this can fluctuate subject to business need).

What you'll achieve

Your key responsibilities will be to:

Responsible for the practical delivery of a range of data engineering activities within the central data team to produce relevant data models to support business reporting requirements
To support the lead data engineer in the maintenance and development of Orbit’s central data platform including responding to technical issues and supporting the deployment activities across the development, test and production environments
Embed and document best practice processes within the Data Engineering team
Utilise Azure DevOps in line with Data Engineering ways of working for source control, development, deployment pipelines and testing. Ensuring any assigned DevOps activities have the required level of detail and time spent on the task is logged
Review requirements, specifications and define test conditions. Identify issues and risks associated with work while being able to analyse and report test activities and results.What you'll bring

To be successful in this role of Junior Data Engineer you will need to have excellent analytical skills with attention to detail, you will also need experience working on SQL database development solutions within Visual Studio.

Essential skills

Excellent analytical skills and attention to detail
Experience working on SQL database development solutions within Visual Studio.
ETL skills (min SSIS) preferably Data Factory and Synapse pipelines
Knowledge of developing data warehouse solutions, modelling Fact and Dimension tables.
Working knowledge of Azure DevOps methodologies for Agile projects. Using DevOps for source control, development, testing and deployment pipelines.
Good communication skills able to convey complex technical concepts to non-technical stakeholders
Experience gathering requirements, doing business analysis and working with SME’s to build/refine a requirements backlog.
Ability to write technical documents and data dictionariesDesirable skills

Experience of D365 and ActiveH systems
Knowledge/experience of Microsoft Purview
Knowledge of Housing Association/Local Authority or Property Development datasets
Familiar with Azure architecture for development and deployment of data infrastructure
Knowledge and/or practical application of Data Vault MethodologyWhy Orbit?

Choosing us means being rewarded in every sense.

Here’s what you can expect to enjoy with us.

A rewarding experience that works for you

We strive to create an inclusive experience with benefits and wellbeing programmes designed to help you, and your loved ones, to thrive. For a better work life balance, we offer flexible working opportunities for many roles.

A place to progress

From training programmes to professional qualifications, we provide opportunities to learn and develop at every stage of your career. Whether you’re a student, graduate or experienced professional we’ll support you to grow.

For leaders, our tailored development journeys are designed to stretch and strengthen your leadership skills. As well as practical training, we give you access to renowned business schools and experiential programmes for greater breadth and depth of learning.

A purpose to feel proud of

We’re proud to make a difference to people together. We’re values-driven with a commercial focus on performance - because the more profit we make, the more we can achieve for people.

What brings us together is a passionate belief in progress and people.

Read more about the values and purpose that drive us on our careers website.

How we hire

We aim to make our hiring process simple and fair:

Online application
Interview(s)
Decision and offerWe put the safeguarding of our customers, colleagues and contractors at the heart of everything we do and as such, certain roles will be subject to a DBS check

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 Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.