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Quantexa is seeking a Senior Software Engineer to join the Research and Development team and help build the next generation of the Quantexa Platform, a Contextual Decision Intelligence offering that helps organizations solve complex problems in fraud detection, financial crime, data management, and related domains.
You will work on core backend services that process, analyze, and display massive volumes of data to detect anomalous and suspicious activity. The platform serves customers globally and is built on a foundation of functional programming principles and distributed systems architecture.
The tech stack is primarily Scala deployed as microservices, leveraging libraries like Cats and Shapeless for functional and strongly typed programming. The team uses Akka and Monix to build scalable, highly performant distributed systems, with event sourcing and CQRS patterns in the persistence layer to ensure datastores are scalable and flexible.
Quantexa is headquartered in London with significant offices in New York, Boston, Toronto, Sydney, Singapore, and Brussels. The company values Determination, Ambition, Teamwork, and Accountability, and maintains a diverse, inclusive culture with colleagues from 47 nationalities speaking over 20 languages.
You will take pride in designing, building, and delivering high-quality, well-engineered solutions to complex problems. You'll take a big-picture approach to problem-solving, ensuring solutions work well within the wider system architecture.
REQUIREMENTS:
- Commercial Scala and Functional Programming experience (candidates without commercial Scala but with relevant commercial background such as Java plus non-commercial Scala experience may be considered)
- Experience across the full engineering lifecycle: design, implementation, testing, and deployment
- Experience with DevOps tooling and approaches (automation and tooling are core company values)
- Beneficial but not essential: big data technology experience (Spark, Hadoop, Elasticsearch), building production batch data processing pipelines (ETL or analytics), data manipulation and cleansing for data quality/integrity improvement
