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Data Solutions

A Data Engineering centre of excellence

Data Solutions

Our team

We believe that using insightful analytics to understand member behaviour is a critical component of the digital world. This is why we need ambitious, solution-focused specialists who can help us harness technology for the good of our members. We’ve started our journey but have a lot further to go, so there’s never been a better time to join and make an impact.

Current vacancies

Explore our current vacancies and apply for roles at Nationwide.

Data Engineer, Data Warehouse


Location Swindon Head Office
Contract Permanent

View job

There is 1 vacancy in Data Solutions


What we do

The Data Solutions team works closely with business areas throughout our Society to deliver industrialised solutions. We’re actively innovating to drive more value for our organisation and our members, and to be data enabled, not data driven.

Explore other teams in Data & Analytics

Business Intelligence and Data Visualisation

We’re using data to drive better decisions, give us a competitive edge and keep our business sustainable.

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Data Management

Using data to drive better decisions for our members

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Data Science

Realising the opportunities presented by Big Data

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Data Scrabble Tiles Fb

Design Principles

The pace and scope of change in technology can be daunting, but if we design based on sound principles, we have nothing to fear. Rob Kellaway, from our Data Solutions team, looks at the options.

Data engineers are largely tasked with building and maintaining data pipelines for analysts and data scientists to address use cases from the business. This is a relatively immature discipline and accepted design principles are scarce. It is worth exploring the areas that could potentially be used as a framework for design.

Beyond ETL

Data engineering is not just Extract, Transform and Load (ETL). It is about problem-solving and designing for “immutable data”. This is data that does not change in itself, just changes at a point in time. For example, you may know that Boris Johnson is Prime Minister in January 2020, but that same Prime Minister was Theresa May in June 2019. The post of Prime Minister hasn’t changed, just the person who is currently in post.

Alongside immutable data, the design consideration of “idempotent” processing needs taking into account. Idempotent operations means that the same input will consistently produce the same output, with no side effects. This leads to a surity of processing that provides a stable platform for Big Data analysis.

Safe Processing

The likelihood of run-time or configuration errors can be reduced by specifically undertaking unit testing for these scenarios. There is nothing less efficient than compromising the safety of your processing by allowing configuration or run-time errors to creep in.


Being able to effectively reduce the possibility of not being able to debug code for creating data pipelines, design your processes to reproduce the results with confidence. If you relate this to ETL, you should be able to run the same job again on the same data and obtain the exact same results as before.

Validation of Data

The quality of your engineering solution must not be compromised by poor quality data. Therefore, effective data management should be employed to gatekeep what is coming into your pipeline.

Tracing Data Lineage

Data lineage mapping is facilitated by immutable data, which allows a trace on where data has been sourced from, its target state and how it has been transformed in the pipeline.

Building Legendary Service

At Nationwide, we are investing significantly in people and technology to increase the value of our data. We recognise data design is a key tool for managing our data as an asset.


Sounds interesting? We’re committed to listening to our members and making changes to improve our service to them, using data to make enabled decisions. This can be amazingly rewarding.

Check out our job vacancies or start a conversation with us above and join Nationwide’s #data revolution.

Data Lake Fb

Do you like your data curated?

The key to understanding the distinction between lakes and warehouses for data storage is the notion of curated data.

Data lakes are typically not modelled (just files of data from disparate sources brought together in a file system), so data can be pulled together at the last moment to answer the question of the day. This is called ‘’late bind’’. It needs different technologies to work well together and can be difficult to guarantee good performance. However, it can be done with the right effort and investment.

By contrast, in warehouses the data is curated.

Rob Kellaway, Head of Data Design, explores which is better.

Functional Challenge

In fact, it's not possible to say whether lakes or warehouses are better. This is more a question of which is the most cost effective solution and how sure you are that you know the questions you need to ask. These considerations are the ones which will ultimately dictate the choice.

They are two specific methods of storing data for reporting purposes. The data lake is better suited to the needs of data scientists working on use cases in a lab environment. The multiple data sources and the ability to quickly prototype analysis is a natural fit for data scientists to gain insight by bringing them all together to make sense of what is being detailed.

On the other hand, a data lake is not effective for running operational reporting, due to the uncurated data. Data warehouses are usually populated with structured, good quality data, which has been verified at source.

Future Developments

Hybrid systems are being developed, which enable lakes and warehouses to work in tandem via the use of Cloud technology. Rather than always productionise in the data warehouse, we will see an eco-system of technologies emerge, and we will pick the right platform, for the right reason, at the right price point, and ensure consistent governance over our data.

Data Engineering

Data engineers will need to continue to work with data scientists to ensure that hybrid systems work well together.

At Nationwide, we are looking for data engineers and data scientists to take us further on a journey to provide legendary service and to continue to serve our members in the best way possible.

Sound interesting?

If you’re looking for a new challenge in your career, check out our opportunities above and 'start a conversation' about joining Nationwide’s Data & Analytics Community.



First Image Of Black Hole National Science Foundation

Data Engineering and the Black Hole

There is a buzz around data engineering. Nationwide Building Society is recruiting a significant number of data engineers, and with good reason.

Data scientists have hit the headlines with the release of the picture of the Black Hole. However, a data scientist is only as good as the data they have access to. Rob Kellaway, Head of Design, explores the part Data Engineers play.

Mature organisations will store their data in variety of formats across a variety of platforms. This is where data engineers come to the fore. They are responsible for building processes that transform that data into formats that data scientists can use. Data engineers are as important as data scientists but tend to be less visible, largely because they are further from the end product, the analysis.

Boldly going where no data scientist has been before

Data engineers are a key component in any organisation’s drive for efficiency. They make the job of extracting value from multiple data sources possible. Data engineering is the foundation upon which silos of data can be networked together, ensuring that the quality, accessibility and timeliness of the resource is appropriate to convert into tangible value for the organisation. And greater automation of processes supports the work of data engineers, making pipelines of data sources more efficient for data scientists to use.

At Nationwide, we are investing significantly in people and technology to increase the value of our data. We recognise data engineering is a key component of our Data Strategy.

We are also looking at DataOps, which is the extension of DevOps values and practices into the analytics world. The DevOps philosophy is about seamless collaboration between developers, quality assurance teams and IT Operations. DataOps does the same for the admins and engineers who store, analyse, archive and deliver data.


At Nationwide Building Society, we are committed to listening to our members and making changes to improve our service to them, using data to make enabled decisions. Saving our members’ time and money is what we do.

Sound interesting? Check out our job vacancies and see if joining the data revolution is for you.