As Google Cloud's premier partner in AI, we provide world-class businesses with cutting-edge data solutions in the cloud. We help clients take leading technology to the limits by combining our expertise in machine learning, data engineering, and analytics. With Google Cloud Platform as our foundation, we help businesses future-proof their solutions, deepen their understanding of consumers, increase competitive advantage and unlock operational efficiencies.
Our team consists of experts in machine learning, data science, software engineering, mathematics, and design. We share a passion for data & analysis, operate at the cutting edge, and believe in a pragmatic approach to solving hard problems.
As a Data Engineer you will work with colleagues on the data engineering and architecture part of our consulting projects, building robust pipelines and wrangling data in a way that it becomes easy to visualise and ready to be fed into our machine learning models. In addition to that, you will be helping us to build out the data engineering side of our next-generation machine learning products.
Furthermore, you will:
The Basics: 25 days holiday in addition to bank holidays, choice of laptop, pension scheme (rises by an extra 1% per year of service), team bonus payable annually, tech scheme, home equipment allowance
Health & Wellbeing: Private Healthcare Insurance, Employee Assistance Program, Discounted Gym Membership, Cycle to Work and Tech Scheme
Learning: Datatonic encourages continuous learning at all levels with a generous conference budget, freedom to explore the latest tools and technologies as well as regular knowledge-sharing activities. You will get access to Linux Academy, Coursera, and LinkedIn Learning and a £700 Conference Budget
Career Development: A personalised development plan to ensure you hit your professional goals with a clear roadmap for progression
Impact: The opportunity to work on cutting-edge AI and ML solutions spanning multiple industries and with market-leading organisations
Innovation: Access to Datatonic LABs, our Research & Development hub. Experiment and bring forward ideas, create impactful and meaningful work in a creative and collaborative environment - even just for fun!
Office Environment: A modern office set in the innovation hub of Canary Wharf with complimentary fruit, cookies, tea/coffee throughout the day as well as a shared café and working space with panoramic views of London
Team Vibe & Social: A welcoming and friendly team plus regular monthly social events and team off-sites including Tasty Tuesdays (paid lunch) and Thirsty Thursdays (team drinks).
WORKING FOR DATATONIC
Our UK headquarters is based in a tech-centric environment in the centre of Canary Wharf. We also have offices in Stockholm, Geneva, Munich, and Barcelona, and we work with clients in many other European countries. We allow for flexible working hours and the possibility of remote work.
We’re an eclectic team with lots of different interests and personalities. We have many different roles across data science, engineering, analytics, consulting, marketing, business development, and operations. As a company, we have a strong emphasis on learning and professional development. Our core values are:
A hub for continuous learning, curiosity, developing ideas, testing things out - and then putting ‘excellence’ into practice.
Working collaboratively in cross-functional teams to achieve customer delight. We love what we do and draw immense satisfaction from taking responsibility and seeing the results of our work.
Creating positive change for our customers, using the most relevant machine learning and analytics approaches and the best technologies to meet real business needs.
Winning with Partners & Friends
Our business has been built on great partnerships and open-source collaboration. It’s one thing to be successful, but to do it in partnership with great people and other great companies is even better in our opinion.Apply for this job
Know exactly where and how to start your AI journey with Datatonic’s
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* Duration dependent on data complexity and use case chosen for POC model
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