Data Engineer, Communications Intelligence & Insights

Who we are:

Burson, part of WPP, is the global communications leader built to create value for clients through reputation. With highly specialized teams, industry-leading technologies and breakthrough creative, we help brands and businesses redefine reputation as a competitive advantage so they can lead today and into the future. When you work at Burson, you are part of a global community of lifelong learners who thrive at the edge of innovation.

WPP (LSE/NYSE: 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

For more information visit bursonglobal.com and follow us on LinkedIn and Instagram .

More About the Role

Are you interested in building the systems that underpin how modern communications intelligence is measured, analysed and scaled?

Burson’s EMEA Intelligence & Insights Hub is a centralised production and engineering capability responsible for developing the data infrastructure, pipelines and workflows that power monitoring, measurement and reporting across the region. Bringing together engineers and analysts, the Hub builds scalable platforms, integrates multiple external data sources and develops AI-enabled workflows that enhance both the efficiency and quality of intelligence delivery.

As a Data Engineer, you will play a key role in developing, maintaining and optimising the engineering systems that sit at the heart of the Hub’s delivery model. You will work across core platform engineering, client-specific solutions and AI workflow development, helping to evolve The Fount and Burson’s wider Data & Intelligence technology stack.

Our technology ecosystem is built on a modern, highly integrated cloud and relational database architecture. Core data-processing pipelines are developed using object-oriented Python and run through scalable cloud-compute orchestration and enterprise ETL tools. Managed SQL environments provide our relational system of record, while raw and semi-structured assets are maintained within secure cloud object storage.

Advanced language models are integrated directly into our processing pipelines to automate enrichment at scale, with the wider ecosystem managed through GitHub, Azure DevOps and Infrastructure-as-Code.

This is an opportunity for an engineer who enjoys solving complex data challenges while building reliable, scalable and reusable systems that support analysts, client teams and global stakeholders.

What you'll do:

Build, develop and maintain Burson’s core data platform and pipelines , supporting scalable ingestion, transformation, storage and delivery of intelligence data across the EMEA Hub.

Design and manage robust ingestion pipelines for media monitoring, social listening, APIs, internal datasets and third-party research and analytics sources.

Develop transformation and data-modelling layers using SQL and Python, creating both standardised central data models and bespoke client datasets that support downstream reporting, including Power BI.

Design and deliver client-specific data engineering solutions , developing custom pipelines, integrations and datasets where off-the-shelf technology does not meet complex or specialist requirements.

Optimise the performance, scalability and reliability of data systems , identifying bottlenecks, resolving data-quality issues and continuously improving pipeline efficiency.

Develop reusable engineering frameworks, templates and components that enable the Hub to scale solutions consistently across clients, markets and use cases.

Contribute to AI-enabled workflows , embedding language models into data pipelines to automate content analysis, classification, tagging, summarisation, relevance, sentiment and other enrichment processes.

Develop and refine prompting frameworks and AI-processing workflows , including monitoring and optimising token usage to ensure effective, efficient and commercially sustainable AI enrichment at scale.

Partner with analysts, product teams and communications specialists to translate approved business and reporting requirements into clearly defined technical solutions and identify appropriate opportunities for AI and automation.

Work within a structured Agile and ticket-driven engineering environment , contributing to sprint planning, daily stand-ups and technical design discussions while delivering against agreed functional specifications and architectural standards.

Maintain strong engineering governance and security standards , including consistent code practices, Git workflows, secure credential management, database security and adherence to agreed directory and repository structures.

Own high-quality technical documentation , maintaining clear technical guides, markdown documentation, database schemas and system records while contributing to planned refactoring and technical-debt reduction without disrupting production services.

Experience that contributes to success:

Strong experience in data engineering , with a track record of designing, building and maintaining reliable production-grade data pipelines and platforms.

Advanced SQL skills and strong experience in relational database environments, including data transformation, modelling, optimisation and the creation of data structures for downstream analytics and reporting.

Strong Python development capability , including writing clean, readable and object-oriented code, working with pandas and developing concurrent or scalable data-processing workflows.

Hands-on experience with APIs, data integrations and ETL/ELT architectures , with the ability to bring together structured and semi-structured data from multiple sources.

Experience working within cloud-based data environments , ideally Microsoft Azure, including cloud compute, managed databases, object storage and enterprise data-processing services.

Strong software-engineering discipline , including experience designing and writing automated unit, integration and regression tests in Python, ideally using pytest .

A strong understanding of secure engineering practices , including database security, firewalls and secure secrets and credential management using tools such as Azure Key Vault .

Experience with Git-based engineering workflows and CI/CD environments , ideally including GitHub and Azure DevOps, with an appreciation for structured branching, code governance and multi-developer environments.

Familiarity with Infrastructure-as-Code , ideally Terraform or Terragrunt, and the ability to safely manage or update environment variables, configuration and access controls within established infrastructure frameworks.

Experience or strong interest in AI-enabled data engineering , including exposure to large language models, prompt-based workflows, automated enrichment or the integration of AI services into production data pipelines.

Strong analytical, troubleshooting and technical-planning skills , with the ability to diagnose complex data and performance issues, scope engineering work effectively and build scalable solutions rather than short-term fixes.

A collaborative and disciplined approach to engineering , comfortable working across multiple projects with analysts, engineers, product teams and business stakeholders while maintaining strong documentation, continuous improvement and adherence to agreed technical standards.

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