Merkle is a leading data-driven, technology-enabled, global performance marketing agency that specialises in the delivery of unique, personalised customer experiences across platforms and devices. We call it ‘people-based’ marketing, and with over 25 years’ experience, we are proud to be recognised as a global leader.
Merkle’s heritage in data, technology and analytics is the foundation for our understanding of consumer insights that drives our people-based marketing strategies. Combined with our expertise in performance creative and media, we can then offer our clients content-driven, contextual and compelling customer experiences that drive business growth.
In 2016, the agency joined dentsu, one of the world’s biggest media companies to form the Customer Experience Management (CXM) Line of Business.
We are looking for exceptional candidates to join our growing Data Engineering team at Merkle to focus on delivering solutions to clients. Successful candidates will understand modern data platform architecture and have solved complex technical problems to deliver to clients’ requirements. You will be a competent team player, developing simple and straightforward solutions that are scalable, utilizing appropriate technologies and engineering best practices.
Under the direction of a Solutions Architect, the post-holder will:
- Design great data solutions for our clients
- Work closely with local leadership as well as our global teams, bringing their key industry and product expertise and knowledge, to shape and deliver client solutions.
- Support our Data Scientists and Analytical Consultants working on projects
- Provide technical expertise and consultancy to clients to help them understand the power of big data analytics for their organization
In this role you will have the opportunity to work with clients from a wide range of sectors, understand their specific requirements, while liaising with data architects, data scientists and analytical consultants in order to design, develop and deliver the optimal technical solution.
As part of the Data Engineering team, you will be expected to take an active role in sharing knowledge and experiences with your team-mates through informal showcases and demonstrations
What we are looking for in you:
You will have a good understanding of distributed computing, data and application architectures, networking, security, and infrastructure management. You produce great code to drive production-grade data pipelines with durability and elasticity as standard. .
- Implementation of Data Lake patterns in Azure
- Understanding of Data Lake partitioning policies and role based access controls
- ETL Development using well-architected frameworks
- Building cloud data solutions using optimised code, repeatable, reusable techniques
- Dev Ops such as Continuous Integration, Automation, Infrastructures as Code
- Cross functional team work internally and with external clients
- A knowledge of cloud technologies in AWS, Azure and/or GCP with a focus on data engineering; optimising data, pipelines, ETLs, data modelling and analytical tools are a plus
Above all you will be a curious, driven individual with a passion for data, ready to learn and develop your skills in an exciting, empowered and challenging role.
We are also open to reviewing candidates that require a sponsorship to work in the UK providing that they match up with our key requirements and are eligible to be sponsored under our guidelines.
Merkle does not discriminate against job applicants on the basis of age, disability, gender reassignment, marital or civil partner status, pregnancy or maternity, race, colour, nationality, ethnic or national origin, religion or belief, sex or sexual orientation. Experience stipulated in this job description serves as a guide only and all applications will be considered on their merits, irrespective of experience.
As part of our Diversity and Inclusion agenda, and as an Equal Opportunities employer, if you require reasonable adjustments during the selection process please engage directly with your Recruiter