Their people are truly at the heart of everything this company does. From their colleagues delivering ground-breaking solutions to the customers who use them: people have helped them grow for more than thirty years, and people are driving their future as a great SaaS company. They’re writing their next chapter. Be part of it!

Our client recognize that the world of work has rapidly shifted over the last few years, particularly how we work. That is why they have committed to working in a hybrid way going forward. Human connection is an essential ingredient of the 4 principles that make up their Flexible Human Work hybrid framework and they want to be transparent in what that looks like when you join their family. On one hand, their offices will continue to play an important role in their future and serve as a place for spontaneous conversations, connection, collaboration as well as focused time. On the other hand, they have learned to reimagine where and when they work and to unlock that flexibility and innovation for their colleagues offering them the opportunity to work flex across their home, company offices or customer sites.

Truly a company that focuses on their Diversity & Inclusion policy and wants to improve their female employee to male employee ratio #womenintech

The role…

• Work closely with our Data Scientists and ML engineers to build data warehouses and pipelines

• Collaborate with our AI Infrastructure team to extend the capabilities of our machine learning platform

• Monitor and optimize the quality and performance of our data pipelines and tools

• Collaborate with other teams to integrate new data sources, ensuring privacy and security protocols are followed

• Collaborate with data scientists and ML engineers to design and build data analytics tools for detecting data anomalies and ensuring data quality

You will have…

• 5+ years of relevant practical experience working on data integration, data modelling and metadata management

• Database experience – you know how to write and optimize SQL queries, design efficient schemas for OLAP and OLTP and understand differences and tradeoffs between them

• Expert knowledge and experience with several relevant languages, tools and frameworks: e.g. Python, SQL, Scala, Spark, Beam, Hadoop, Kafka, RabitMQ, Athena, Amazon EMR etc

• Excellent written and verbal communication skills, and ability to evaluate and explain technical details clearly

• Experience in building large scale ETL/ELT processes, datalakes and data warehouses

• Willingness to adapt to significant changes in either technology or environment

• Previous experience working with financial data is a plus

Benefits…

• 15% bonus (50% personal performance, 50% business performance)
• 25 days holiday + 8 bank holidays + 5 learning days + 5 charity days
• Up to 18% total pension with top ups
• 50% income protection for 2+ years
• Flexible working, hybrid or fully remote
• Paternity and maternity leave
• Medical insurance salary sacrifice, cycle to work, mental health memberships, gym classes + more