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.

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.
Propose technical designs and develop integrations.
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.

Requirements

Minimum requirements for this role include:

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.