• Due to the continuous success and high demand from our customers, we are looking for s Software Engineer to join our R&D team to build and shape the next generation of products that transform how organizations around the world use data
• You’ll be joining the Research and Development department where you will be working on our core Contextual Decision Intelligence offering: The Quantexa Platform. The Quantexa Platform helps our Customers solve business problems in areas such as fraud, financial crime, data management and many more. Our products process, analyse and display huge volumes of data with the ultimate aim of detecting anomalous and suspicious activity
• Our back end services are primarily written in Scala and deployed as microservices. We are huge fans of functional and strongly typed programming, making use of libraries such as Cats and Shapeless
• We use Akka and Monix to help build scalable, highly performant, distributed systems. We utilise techniques such as event sourcing and CQRS in our persistence layer to ensure our datastores are scalable and flexible

Benefits

• Pension Scheme with a company contribution of 6% (if you contribute 3%)
• 25 days annual leave (with the option to buy up to 5 days) + birthday off!
• Health & Wellbeing: Private Healthcare, EAP, Well-being Days, Calm App, Gym Discounts
• Tech & Cycle-to-Work Schemes

• Take a big picture approach to solving problems, taking care to ensure that the solution works well within the wider system
• Take pride in designing, building and delivering high quality well engineered solutions to complex problems
• Experience with DevOps tooling and approaches (automation and tooling is something we are passionate about, we even have a dedicated Developer Tooling team)
• Commercial Scala and Functional Programming experience (we may consider candidates without if their commercial background is relevant e.g. Java and they have some non-commercial Scala experience)
• Have experience of the full engineering life cycle, from design and implementation through to testing and deployment
• Working with big data technology, ideally Spark but others will also be useful such as Hadoop or Elasticsearch
• Building data processing pipelines for use in production batch systems, including either traditional ETL pipelines and/or analytics pipelines
• Manipulating data through cleansing, parsing, standardising etc, especially in relation to improving data quality/integrity