Jobs / India / WPP 2005 Limited
Senior Data Engineer
WPP 2005 Limited · 🌍 India
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About the role
WPP is the trusted growth partner for the world’s leading brands. We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. We work with the world's most valuable brands and have global reach across 100+ markets, with deep local expertise. Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow. For more information, visit WPP.com. Why we're hiring: We are seeking a highly skilled and experienced Senior Data Engineer to join our growing data team. In this critical role, you will be instrumental in designing, building, and optimizing our scalable data lakehouse platform using Google BigQuery or Databricks. You will be a key player in developing robust data pipelines that ingest data from various sources, including Google Analytics 4 (GA4), and transform it into reliable, analysis-ready datasets within the lakehouse environment. This role requires deep expertise in modern lakehouse platforms – Google BigQuery and/or Databricks – together with strong skills in SQL, Python, and Apache Spark (PySpark), along with strong hands-on experience across Azure, AWS, and GCP cloud environments, as our data ecosystem spans multiple cloud platforms. You will be responsible for the entire data lifecycle within the lakehouse, from ingestion and transformation to governance and optimization, ensuring data quality and performance. You should be adept at analyzing performance bottlenecks in Spark jobs and BigQuery workloads, providing enhancement recommendations, and collaborating effectively with both technical and non-technical stakeholders. What you'll be doing: • Design, build, and deploy robust ETL/ELT pipelines within the lakehouse platform (Google BigQuery or Databricks) using SQL, Python, PySpark, and Spark SQL. • Implement and manage the Medallion Architecture (Bronze, Silver, Gold layers) using Delta Lake or BigQuery datasets to ensure data quality and progressive data refinement. • Leverage native ingestion tooling – such as BigQuery Data Transfer Service, Pub/Sub streaming, or Databricks Auto Loader – for efficient, scalable, and incremental ingestion of data from sources like GA4 into the Bronze layer. • Develop, schedule, and monitor complex, multi-task data workflows using Cloud Composer (Airflow), BigQuery scheduled queries, or Databricks Workflows. • Optimize BigQuery tables (partitioning, clustering, materialised views) and Spark jobs / Delta Lake tables (using techniques like OPTIMIZE, Z-ORDER, and partitioning) for high performance and cost efficiency. • Implement data governance, security, and discovery using Dataplex / BigQuery policy tags or Unity Catalog, including managing access controls and data lineage. • Write complex, customized SQL queries to manipulate data and support ad-hoc analytical requests from business teams. • Develop strategies for data ingestion from multiple sources, using various techniques including streaming, API consumption, and replication. • Document data engineering processes, data models, and technical specifications for the lakehouse platform. • Conform to agile development practices, including version control (Git), continuous integration/delivery (CI/CD), and test-driven development. • Provide production support for data pipelines, actively monitoring and resolving issues to ensure the co