Associate Data Engineer
Quantexa
| Company | Quantexa |
| Category | Engineering |
| Location | London |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 13 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
What we’re all about.
It isn’t often you get to be part of a tech company that, since 2016, has been innovating the data analytics market in ways no-one else can. Our technology started out in FinTech, helping tackle serious criminal activity. Now, its potential is virtually limitless. Working at Quantexa isn't just intellectually stimulating. We’re a real team. Collaborating and constantly engineering better and better solutions. We’re ambitious, we think things through and we’re on a mission to discover just how far we can go. Nearly half of our colleagues come from an ethnic or religious minority background. We’re made up of people from 47 nationalities who speak over 20 languages. As a diverse mix of individuals, we make one big unstoppable team.
If our incredible culture sounds like you, we’d love you to join us.
What will you be doing?
You’ll be joining the Applications team, which is an Engineering function within Quantexa’s R&D department that is focused on internally building real-world applications of the Quantexa Platform.
This function enables demonstrations of the product and develops SaaS offerings, whilst also testing and refining new Platform features before they are deployed by clients. The function develops and releases its own tools that feed into these internal applications and are also packaged and released to help standardize and accelerate all Quantexa deployments. It encompasses four distinct sub-teams:
Data Engineering Accelerators:
- Developing Quantexa’s libraries for cleansing, parsing and standardising data used in entity resolution
- Finding efficiency/performance improvements through big data testing and building performance tooling
- Owning best practices in entity resolution and network building
Data Feeds:
- Building standardised and reusable code for processing various third party/open source data sets
- Managing an internal data lake for the provision of this data by other teams for testing and analytics
- Owning general best practices for ingesting and processing data to get it ready for use in the Quantexa Platform, including pipelines and scheduling
Demos:
- Developing, deploying and maintaining all Quantexa demos, showcasing the different use cases for the Quantexa Platform
- Owning the Quantexa Trial platform, for prospective Quantexa clients to see the product in action using real data provided by Data Feeds
- Building tools to enable solution owners and sales to create their own custom demos
SaaS:
- Building Quantexa’s emerging SaaS offering, a cloud hosted, standardized deployment of the Quantexa Platform
- Targeting mid-market banks in the US for Retail AML initially, providing them with a cost-effective Quantexa solution, then expanding in future to more use cases and geographies
- Implementing cutting edge features of the Quantexa Platform ensuring SaaS customers always on the latest and greatest of Quantexa
More detailed information on the day to day activities of the four sub-teams can be shared on request. The teams all work together closely and team members are able to rotate between the teams to enable knowledge sharing and personal development.
We are looking for candidates who enjoy the following to join us:
- Data processing/ETL pipelines
- Analysing and examining real and varied data
- Full stack development, but with a heavy focus on the data processing/ETL side
- Solving difficult problems with efficient, resilient, high impact code
- Working in the cloud with production-grade systems
- Defining best-practices and sharing expertise you’ve developed
- Working in a fast moving, Agile environment
- Growing and thriving within one of the UK’s fastest growing scale-ups
Experience in the following would be beneficial:
- A strong coding background, ideally in Scala or otherwise in a relevant language that will allow you to learn Scala quickly (e.g. Java/Python)
- Big data, either from a s
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