Job Description

WPP is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients, and communities.


Working at WPP means being part of a global network of more than 115,000 accomplished people in 110 countries. WPP has headquarters in New York, London and Singapore and a corporate presence in major markets worldwide.


We create transformative ideas and outcomes for our clients through an integrated offer of communications, experience, commerce, and technology.


WPP and our award-winning agencies work with most of the world's biggest companies and organisations - from Ford, Unilever and P&G to Google, HSBC, and the UN. Our clients include 61 of the FTSE 100, 307 of the Fortune Global 500, all 30 of the Dow Jones 30 and 62 of the NASDAQ 100. WPP are the leader in the Bloomberg Gender Equality Index and 20th in the FTSE 100 rankings for Women on Boards.

Why we're hiring:



As a Data Engineer in the WPP Enterprise Data Group, you will work with the Impact Analytics team to design and implement scalable data solutions, providing enterprise-scale data transformation across a broad range of projects. As part of a team, your role will be focused on delivering solutions that leverage large scale data ingestion, processing, storage/querying, streaming & batch analytics using Azure Databricks, as well as other Azure based technologies. You will be joining a group of Data Engineers who are passionate about building the best possible solutions for our business and endorse a culture of life-long learning and collaboration.


What you'll be doing:


• Design & build data pipelines in Databricks using PySpark/SQL, according to business and technical requirements
• Deliver data migrations between legacy platforms & Azure Databricks
• Propose technical designs and develop integrations
• Design & deliver data warehousing solutions and key data engineering workstreams for any required solution
• Support cross-functional teams across the data space


What you'll need:



Minimum requirements for this role include:


• 3+ years of experience in Python and SQL
• Experience using Databricks
• Experience of Microsoft Azure data services -ADLS gen2, Azure Key Vault, Data Factory, Synapse
• Ideally experience in the following -Delta Lake, PySpark
• Fluency in English
• Exposure to data science / ML is a plus




Who you are:


You're open •: •We are inclusive and collaborative; we encourage the free exchange of ideas; we respect and celebrate diverse views. We are accepting: of new ideas, new partnerships, new ways of working.


You're optimistic •: • We believe in the power of creativity, technology and talent to create brighter futures or our people, our clients and our communities. We approach all that we do with conviction: to try the new and to seek the unexpected.


You're extraordinary: we are stronger together: through collaboration we achieve the amazing. We are creative leaders and pioneers of our industry; we provide extraordinary every day.




What we'll give you:


Passionate, inspired people - We promote a culture of people that do extraordinary work.


Scale and opportunity - We offer the opportunity to create, influence and complete projects at a scale that is unparalleled in the industry.


Challenging and stimulating work - Unique work and the opportunity to join a group of creative problem solvers. Are you up for the challenge?

WPP is an equal opportunity employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability. We believe in creating a dynamic work environment that values diversity and inclusion and strives to recruit a diverse slate of candidates to help us achieve that goal.


Please read our Privacy Notice ( https://www.wpp.com/people/wpp-privacy-policy-for-recruitment ) for more information on how we process the information you provide.