We are building out a large AI-based anti-fraud platform, which is provided to clients on a SaaS platform. We are expanding due to the success of the product, and the size of the client pipeline. Every client project has a strong data engineering component.
You will work with the team in engineering the appropriate data pipelines and environments for our projects.
In particular, you will be developing the means to collect and ingest data, developing data models and data architectures, creating automated data pipelines, and taking the lead in making these Production-ready. You will assist with integrating with existing applications, and will be working with leading organizations and teams.
We are interested in hearing from individuals with a background in data - both SQL and non-SQL, and ideally some scripting background. Exposure to AI & ML would also be beneficial.
You will work alongside a strong, global team of individuals with diverse backgrounds and skills in a variety of areas to:
◆ Analyse data sources, and acquire data
◆ Create data pipelines, and integrate to final data destinations
◆ Create appropriate data models, architectures and pipelines
◆ Move the models and pipelines into Production
You will assist the practice in:
◆ Developing templates and accelerators, across a variety of libraries and platforms.
◆ Participating in pre-sales work and client work as necessary
◆ You will collaborate with business and technology partners to grow and develop the data science practice
Who we're looking for?
◆ Strong data-related development skills, preferably in mainstream versions of SQL and NoSQL. Experience of scripting languages, especially Python
◆ Exposure to the full Software Development Life Cycle, and experience of working in a modern development team
◆ Good analytical skills
◆ Strong communication skills, both verbal and written.
◆ At least some experience with big data
◆ Degree in Computer Science, or equivalent experience
◆ At least two years of data engineering and/or software development experienceNICE TO HAVE
◆ Exposure to analytics, and/or Machine-learning-as-a-service.
◆ Experience in working with / supplying data to visualization tools such as Qlik, Tableau, PowerBI or similar.
◆ Good understanding of data integration patterns
◆ Experience with / exposure to software development for analytic applications
◆ Experience in big data
◆ Experience in projects involving cross-functional teams