Senior Analyst-Data Engineer at WPP Media

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

15 Jun, 26

Salary

0.0

Posted On

17 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, ETL/ELT Pipelines, Azure Data Factory, Azure Data Lake Storage, SQL, Python, API Orchestration, Google Cloud Platform, DBT, Adverity, PowerBI, Data Quality Checks, CI/CD, Code Reviews, Troubleshooting, Data Warehousing

Industry

Advertising Services

Description

About WPP Media WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth. WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working. For more information, visit wppmedia.com. About WPP Media WPP is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients and communities. For more information, visit wpp.com. WPP Media is WPP’s global media collective. In a world where media is everywhere and in everything, we bring the best platform, people, and partners together to create limitless opportunities for growth. For more information, visit wppmedia.com At WPP Media, we believe in the power of our culture and our people. It’s what elevates us to deliver exceptional experiences for both our clients and each other. In this role it will be critical to embrace WPP & WPP Media’s shared core values: Be Extraordinary by Leading Collectively to Inspire transformational Creativity. Create an Open environment by Balancing People and Client Experiences by Cultivating Trust. Lead Optimistically by Championing Growth and Development to Mobilize the Enterprise. This role will serve as core member of a data engineering team assigned to one of WPP’s major global clients, who will be maintaining and expanding upon a global data warehouse, automated reports, and data provisioning applications to ensure that media and marketing stakeholders the world will have access to the right data and insights to inform their marketing decisions. WPP has developed the Measure platform, a stack of standardized technologies (Adverity for collection, DBT for modelling, Google Cloud Platform for orchestration, computing, and storage, and Microsoft PowerBI for visualization) and templates to speed the deployment and simplify support for automated reporting and analytics tools. The team’s primary responsibility is to build and maintain a global Measure-based reporting solution, leveraging existing templates where applicable and building custom collectors, data models, cloud architectures, and/or visualizations when required. The team will automate the collection and transformation of disparate marketing data into a single source of truth, continually improving it to incorporate new data sources, and to derive new insights, to support ever-evolving business demands. As an Senior Data Engineer, your primary responsibilities will include deploying and troubleshooting standardized Adverity data collection templates, QAing landed versus source data using self-developed scripts and manual processes, gathering platform and ad account parameters from upstream stakeholders, working within and contributing to the team building the data warehousing solution, adhering to technical standards and best practices, and contributing to the documentation of custom work. In doing so you will exhibit a foundational understanding of cloud technology and key data engineering skills and knowledge to help your team in this fast-evolving and in-demand industry. Some of the things we’d like you to do: Deploy data collectors in Adverity to gather data from media, agency system, and other sources Development and maintain data quality checks and procedures, helping stakeholders overcome data issues before they impact the quality of the reporting solution Support the translation of user requirements and business needs into technical specifications Be a proficient user of Google Cloud Platform and associated technologies, ensuring your work incorporates industry best practice Monitor automated jobs, troubleshooting data issues as-and-when they arise Support other members of the team responsible for “last mile” transformation and visualization of data within Google Data Studio reports and dashboards Serve as a technical and professional leader for junior staff Provide hands-on support to users of reporting solutions, helping the wider team triage and respond to user queries in a timely manner Attending internal stakeholder meetings, presenting your solutions, and providing updates on your work. Support the development strong working relationships with third-party data providers that we rely on for access to necessary data. A bit about yourself: Key Responsibilities: Design, develop, and optimize enterprise ETL/ELT pipelines utilizing Azure Data Factory, Azure Data Lake Storage, and advanced serverless technologies—ensuring cross-platform scalability, security, and reliability. Implement highly efficient, complex SQL solutions—including advanced query optimization, partitioning, and indexing—to power sophisticated analytics models and enable real-time data processing. Lead the development and integration of robust automation frameworks using Python, and API orchestration, introducing innovative solutions to streamline the entire data lifecycle and maximize operational efficiency. Exercise full ownership of production pipeline reliability; proactively identify and resolve critical issues through deep root-cause analysis, and institute resilient, long-term stability measures for continuous uptime. Establish, refine, and enforce rigorous coding and architectural standards through comprehensive code reviews and technical audits, setting quality benchmarks for the offshore engineering team. Champion best-in-class documentation, data lineage, and automated CI/CD deployment practices, ensuring transparency, auditability, and governance across all data assets. Demonstrate expert-level support for analytics and media teams, translating business challenges into scalable, high-performance technical solutions, leveraging deep Azure and SQL expertise. Senior-Level Expectations: Act as the organization’s subject matter expert (SME) in end-to-end pipeline architecture, orchestration frameworks, schema evolution, and advanced Azure Data Factory strategies. Lead modernization initiatives across the organization, championing the adoption of technologies such as Databricks, DBT and advanced DevOps practices to future-proof the data platform and enable rapid innovation. Ensure strict adherence to SLAs and regulatory compliance through advanced monitoring, proactive risk mitigation, and continuous improvement of operational processes. Required Skills 3-4 years of progressive experience in data engineering, with expert-level mastery of Azure Data Factory, SQL, and cloud data platforms (Azure and/or GCP). Proven expertise in designing, optimizing, and managing large-scale, production-grade data pipelines, including deep skills in SQL performance tuning, ADF orchestration, and CI/CD automation. Advanced knowledge of production operations, troubleshooting, and high-availability data processing at scale. #LI-GOC Please read our Privacy Notice for more information on how we process the information you provide.

Responsibilities

This role involves designing, developing, and optimizing enterprise ETL/ELT pipelines using Azure technologies, while also deploying and troubleshooting standardized data collection templates in Adverity for media data. The engineer will maintain and expand a global data warehouse, automated reports, and data provisioning applications for marketing stakeholders.