Data Engineer - GCP

Confirmed live in the last 24 hours

VML Enterprise Solutions

Mumbai, Maharashtra, India

Remote

Posted March 16, 2026

Job Description

Who We Are

VML, part of WPP, is a leading creative company that combines brand experience, customer experience, and commerce, creating connected brands to drive growth. VML is celebrated for its innovative and human first, award-winning work for blue chip client partners including AstraZeneca, Colgate-Palmolive, Dell, Ford, Microsoft, Nestlé, The Coca-Cola Company, and Wendy's. The agency is recognized by the Forrester Wave™ Reports, as a Leader among Marketing Creative and Content Service Providers, Commerce Services, Global Digital Experience Services, Global Marketing Services and, most recently, Marketing Measurement & Optimization. In addition, VML’s specialist health network, VML Health, is one of the world’s largest and most awarded health agencies. VML’s global network is powered by 26,000 talented people across 55+ markets, with principal offices in Kansas City, New York, Detroit, London, São Paulo, Shanghai, Singapore, and Sydney.

About WPP

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. For more information, visit WPP.com.

We are looking for a Data Engineer with experience working with GCP to join us on a permanent basis. You can either join us in our growing Mumbai team or join us remotely from across India.

Step into the Koppling workstream as a Data Engineer and be a key player in building a sophisticated media data warehouse. Your role will involve developing and deploying robust ETL/ELT data pipelines to automate the integration of 3rd party data sources at scale. This is an excellent opportunity to apply solid technical problem-solving skills, advance your data engineering capabilities, and gain a foundational understanding of the marketing data domain. You'll contribute to creating a centralized, reliable data source that informs critical business decisions, working within a collaborative team environment.

What you’ll be doing:

Developing, and deploying robust ETL/ELT data pipelines using Python and SQL to integrate various 3rd party data sources.
Automating the integration of 3rd party data sources at scale, ensuring data accuracy and timeliness for our media data warehouse.
Utilizing Google Cloud Platform (GCP) services such as BigQuery, Cloud Run, and Scheduler for efficient data processing and orchestration.
Performing data modeling and transformations using dbt and SQL to structure and refine data within the media data warehouse.
Managing and version controlling pipeline code and configurations using Git, ensuring collaborative development and deployment.
Troubleshooting and optimizing existing data pipelines to enhance reliability, performance, and scalability.
Applying infrastructure-as-code principles to manage data resources, potentially using tools like Terraform for environment provisioning.
Developing or integrating APIs for complex data ingestion and exploring the use of streaming data technologies like Kafka or Pub/Sub for real-time data needs.
Implementing and maintaining CI/CD pipelines to automate the testing, deployment, and monitoring of data solutions.

What we want from you:

Professional experience in data engineering or a similar role.
Hands-on experience with Google Cloud Platform (GCP) services including BigQuery, Cloud Run, and Scheduler.
Proficiency in SQL for data manipulation and analysis.

Strong programming skills in Python.

Experience with dbt.
Familiarity with infrastructure-as-code principles.
Understanding of data warehousing concepts, ETL/ELT processes, and data modeling.
Experience with version control systems (e.g., Git).

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