Data Engineer

Das Group
Bristol
2 weeks 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

ARAG UK Group have an exciting opportunity to join our high-performing Digital Services team as a Data Engineer in our Bristol office.

As Data Engineer you will be responsible for supporting the Data Engineering Manager with the design and implementation of the enterprise data lakehouse, data movement and data model design and implementation to support the needs of the business. You will ensure it is trusted, secure, scalable, performant and fit for purpose, as well as:

  • Leveraging contemporary technologies and data paradigms as appropriate
  • Supporting a single analytical view of our data and information
  • Implementing to agreed and defined metrics
  • Delivering business value by supporting action-oriented insights
  • Ensuring it is built in line with our information management strategy and guiding principles.
  • Ensures “explainability” fit for audit in a regulatory controlled environment through appropriate data lineage and documentation.

In our collaborative environment and as part of the data engineering team you will work closely with many areas within the business. Working particularly with Finance, our Reporting & Analytics teams and Data Platform teams and taking responsibility and accountability for the collaborative design and build of data solutions. These will provide a secure, dependable, and well performing platform for information & analytical purposes.

You will be able to confidently address a wide range of different business areas with varied technical understanding whilst adapting your delivery to effectively convey difficult technical problems and solutions to non-technical colleagues. You will have experience implementing cloud centric data solutions using platforms such as Azure and have the ability and desire to pick up new technology, tools, paradigms and develop it further. You are comfortable building out and administrating data models, ensuring that they are accessible and used appropriately whilst leveraging platforms such as Databricks, dbt and Power BI. You will possess the knowledge of automating deployments using a combination of tools such as PowerShell and Azure DevOps and the ability to use Python both to manipulate data and building additional process to support data processing activities is fundamental. You will exhibit a passion for data and information with a strong understanding of data architecture principles and information “story telling” to maximise the value of our raw data. As well as a familiarity with data processing paradigms such as ETL/ ELT, Kimball, Medallion Data Lakehouse.

The successful candidates will have:

  • Experience with multiple ETL/ELT tools, including cloud specific technologies (e.g. Azure DataFactory, dbt, Databricks, SSIS, AWS Glue) desirable.
  • Excellent knowledge of Microsoft SQL Server 2012 onward, including SSIS package design.
  • Strong problem solving and planning skills with a “can do” attitude to manage and mitigate risks to maintain delivery commitments.
  • Knowledge of the insurance industry and working within regulated environments would be desirable.
  • Experience of working within an Agile environment essential, and ability to lead daily stand-up's advantageous.
  • Experience of utilising an ESB (Enterprise Service Bus) desirable (e.g. Mulesoft ESB).
  • Good communication and relationship building skills.
  • Good all round and hands on BI knowledge and skills.

As a team we are passionate and enthusiastic about what we do. Our people are encouraged to think independently and to take ownership of their work. You will be agile in the way that you work and adaptable to change. In return for your commitment, we will offer you generous remuneration and an attractive benefits package which will include:

  • 26 days holiday with the option to buy up to a further 5 days
  • Company pension scheme with the option to increase contributions
  • Progressive career pathway and development opportunities
  • Employee reward and incentive scheme
  • Group Income Protection for all employees
  • Group Legal Protection for all employees
  • A choice of either European Motor Assistance or Home Emergency Assistance
  • Inclusion in our Health Cash plan
  • Salary sacrifice benefits including Cycle scheme
  • A comprehensive wellbeing programme including a range of free weekly exercise classes (dependent on your office location) and free eye tests
  • Access to our employee discounts hub offering exclusive discounts across thousands of retail partners, including discounted gym memberships at over 3,000 gyms across the UK
  • The option to join our Sports and Social club which organises discounted events such as theatre visits and shopping trips

If you think you would be a good match for this role and can demonstrate some transferable experience please apply, regardless of whether you meet all the criteria listed above.


#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 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.