Data Engineer AI, Hybrid

Role Overview: As a Data Engineer in the WPP Enterprise Data Group, you will be responsible for the design and implementation of scalable data solutions providing enterprise-scale data transformation across a broad range of projects. Your role will focus on delivering solutions that utilize large-scale data ingestion, processing, storage/querying, streaming, and batch analytics using Databricks. As part of a team, you will implement world-class solutions designed by our data architects. Your responsibilities will include estimating, designing, coding, testing, deploying, and ensuring scalability and performance on Azure using key technologies like Databricks. As a hands-on technologist with an extensive data engineering background using Databricks, 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 lifelong learning and collaboration. Key Responsibilities: - Design, build, test, and maintain data pipelines (ELT), according to business and technical requirements. - Implement secure platforms with data governance in mind. - Play a key role in automation and building industry-leading solutions against architectural best practices. - Deliver data migrations between legacy & modern platforms. - Design & deliver data warehousing solutions and key data engineering workstreams for any required solution. - Support cross-functional teams across the data space. Qualifications Required: - 3+ years of experience designing and building scalable distributed data pipelines and dimensional data models. - 3+ years of experience in Python and SQL. - Experience using Databricks platform and PySpark is a must. - Extensive experience of Microsoft Azure data services Data Factory, ADLS gen2, Event Hubs, Azure SQL, Azure Key Vault. - Ideally experience in the following Kafka, Delta Lake, Pandas. - A demonstrable understanding of Continuous Integration, Continuous Delivery (CI/CD) and Agile practices, unit & integration tests and development practices using Azure DevOps. - Fluency in English. - Exposure to AI & ML technologies is a plus. Note: The job description also includes additional details about the company's culture, values, and work environment, emphasizing a commitment to fostering creativity, technology, talent, respect, inclusivity, and career growth opportunities. Role Overview: As a Data Engineer in the WPP Enterprise Data Group, you will be responsible for the design and implementation of scalable data solutions providing enterprise-scale data transformation across a broad range of projects. Your role will focus on delivering solutions that utilize large-scale data ingestion, processing, storage/querying, streaming, and batch analytics using Databricks. As part of a team, you will implement world-class solutions designed by our data architects. Your responsibilities will include estimating, designing, coding, testing, deploying, and ensuring scalability and performance on Azure using key technologies like Databricks. As a hands-on technologist with an extensive data engineering background using Databricks, 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 lifelong learning and collaboration. Key Responsibilities: - Design, build, test, and maintain data pipelines (ELT), according to business and technical requirements. - Implement secure platforms with data governance in mind. - Play a key role in automation and building industry-leading solutions against architectural best practices. - Deliver data migrations between legacy & modern platforms. - Design & deliver data warehousing solutions and key data engineering workstreams for any required solution. - Support cross-functional teams across the data space. Qualifications Required: - 3+ years of experience designing and building scalable distributed data pipelines and dimensional data models. - 3+ years of experience in Python and SQL. - Experience using Databricks platform and PySpark is a must. - Extensive experience of Microsoft Azure data services Data Factory, ADLS gen2, Event Hubs, Azure SQL, Azure Key Vault. - Ideally experience in the following Kafka, Delta Lake, Pandas. - A demonstrable understanding of Continuous Integration, Continuous Delivery (CI/CD) and Agile practices, unit & integration tests and development practices using Azure DevOps. - Fluency in English. - Exposure to AI & ML technologies is a plus. Note: The job description also includes additional details about the company's culture, values, and work environment, emphasizing a commitment to fostering creativity, technology, talent, respect, inclusivity, and career growth opportunities.