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Data Engineer

Map LondonEst. Est. GBP 60,000–90,000 / yearSenior

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Python
  • Javascript
  • Sql
  • Spark
  • Airflow
  • Dbt
  • Aws
  • Azure
  • Gcp
  • Kubernetes
Who We Are MAP | WPP Enterprise Solutions designs, builds, and operates the growth systems that competitive businesses rely on. In a world where Al is reshaping how companies drive growth, we lead clients in business transformation and marketing modernization, connecting strategy directly to execution. Our 12,000 experts in engineering and platforms, commerce, consulting, content transformation, CRM, and CX work within a unified global operating unit across 40+ markets. WPP Enterprise Solutions works alongside best-in-class partners including Adobe, AWS, Braze, Google, Microsoft, Salesforce, and Shopify, as well as innovators in AI, to deliver growth solutions tailored to the needs of our clients’ businesses. About WPP MAP is part of WPP , the trusted growth partner for the world's leading brands. Powered by exceptional talent and our agentic marketing platform WPP Open, WPP unites cutting-edge media intelligence and data solutions, creativity, production, enterprise solutions and expert strategic counsel. Are you a Data Engineer passionate about building, supporting, and maintaining digital marketing solutions that deliver real value? Are you interested in working with the largest global brands and complex challenges in the media analytics space? Then you might be the Senior Data Engineer we’re looking for! What will your day look like? As our new Data Engineer, you will become part of our growing Data Insights and Science team. Here, you will employ new technologies across multiple cloud platforms to help successful brands reach their next level in 1:1 retargeting, communication, and CRM. More specifically, your tasks include: Identify, collect, and integrate data from various sources by building high-quality data pipelines and data models for analytics and business intelligence (BI) purposes. Develop and optimize code to enable pipelines at minimum cost and ease of maintenance. Build monitoring procedures & tools to ensure solid ETL flows and data quality. Design processes and tools to correct ETL incidents. Collaborate with CRM developers, data scientists, data analysts, and product owners to ensure the supplied data supports the business initiatives. Consult our data analytics teams to ensure best practices on the technical use of data are followed. Design data architectures and collaborate in data migrations in cloud environments. Who are you going to work with? You will join a team of highly skilled Architects, Data Scientists, and Consultants who are passionate about unlocking insights from data through analytics. You will also get to work closely with experts from other Technology, Creative, and Client Teams. What do you bring to the table? As a person, you have a team player mindset and an open-minded attitude. You can communicate ideas and technical topics honestly and clearly – also to non-experts – while respecting the views of others on the team. You are eager to understand and find solutions, allowing you to quickly adapt to changing situations and come up with new ideas. At the same time, you solve problems in an analytical and pragmatic manner. Moreover, you have: Experience in data engineering, big data, business intelligence, or data science. Experience in Spark, Python, Scala, or similar. Excellent SQL skills enabling large-scale data transformation and analysis. A comprehensive understanding of cloud data warehousing, data pipelines and data transformation (extract, transform, and load) processes and supporting technologies such as Google Dataflow, Looker, DBT, EMR, CI/CD Pipelines, Airflow DAGs, and other analytics tools. Experience with cloud-based data infrastructures (Ideally GCP, but AWS or Azure would also suffice) Programming experience in Javascript, Python, or similar. Expertise in managing databases, including performance tuning, backup, and recovery. Solid knowledge of data quality management best practices, including data profiling

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