Data Engineer (Communications Intelligence & Insights)

Quick Facts

• Role: Data Engineer (Communications Intelligence & Insights)
• Focus: Scalable data infrastructure, pipelines, and AI-enabled enrichment workflows for communications intelligence
• Working mode: Hybrid

Description

You’ll build and optimize the data infrastructure, pipelines, and workflows that enable communications intelligence monitoring, measurement, and reporting across the EMEA hub. The work combines scalable ingestion and transformation with AI-enabled workflows that automate enrichment at scale.

Responsibilities

• Build, develop, and maintain core data platform pipelines for ingestion, transformation, storage, and delivery.
• Design ingestion pipelines for media monitoring, social listening, APIs, internal datasets, and third-party research/analytics sources.
• Create transformation and data-model layers using SQL and Python, supporting both central standardized models and bespoke client datasets (including Power BI).
• Deliver client-specific data engineering solutions with custom integrations and datasets when required.
• Optimize performance, scalability, and reliability; troubleshoot data-quality issues and improve pipeline efficiency.
• Develop reusable engineering frameworks, templates, and components for consistent scaling across clients and markets.
• Contribute to AI-enabled workflows by embedding language models into pipelines for classification, tagging, summarization, relevance, sentiment, and enrichment.
• Refine prompting frameworks and AI workflows, including monitoring and optimizing token usage.
• Partner with analysts and communications specialists to convert business and reporting requirements into technical solutions.
• Work in a structured Agile, ticket-driven environment aligned to functional specifications and architectural standards.
• Maintain engineering governance and security standards, including secure credential management and database security.
• Own technical documentation and support planned refactoring and technical-debt reduction without disrupting production services.

Requirements

• Proven data engineering experience delivering reliable production-grade pipelines and platforms.
• Advanced SQL and strong relational database skills (transformation, modeling, optimization, analytics/reporting structures).
• Strong Python development experience, including object-oriented code, pandas, and scalable/concurrent workflows.
• Hands-on APIs and data integration experience with ETL/ELT architectures handling structured and semi-structured data.
• Cloud data environment experience, ideally Microsoft Azure (compute, managed databases, object storage, enterprise services).
• Experience writing automated unit, integration, and regression tests in Python (ideally pytest).
• Secure engineering practices knowledge, including secrets/credential management (e.g., Azure Key Vault) and database security.
• Git-based workflows and CI/CD experience (ideally GitHub and Azure DevOps) with strong code governance.
• Infrastructure-as-Code familiarity (ideally Terraform or Terragrunt) and safe management of configuration and access controls.
• Experience or strong interest in AI-enabled data engineering (large language models, prompt-based workflows, automated enrichment).
• Strong analytical and troubleshooting skills for complex data and performance issues.
• Collaborative, disciplined engineering approach with strong documentation and continuous improvement.

Benefits

• Hybrid working model
• Access to a suite of generative AI tools
• Fully funded private healthcare package
• Benefits platform with a flexible cafeteria system (including Medicover Sport)
• Clear career path and professional development through global and local training programs
• EAP