Data Engineer, Communications Intelligence & Insights

Burson; Województwo mazowieckie; Hybrid; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Fully funded private healthcare

Hybrid working model

Generative AI tools access

Career development programs

Employee assistance program (EAP)

Job summary

Burson is seeking a Data Engineer to design, build and maintain scalable data pipelines and platforms that power intelligence delivery across EMEA. You will work on data ingestion, transformation and modelling using SQL and Python, and you will integrate AI-enabled workflows into production data paths.

The role involves collaborating with analysts, product teams and clients, operating in a structured Agile environment, and adhering to security and governance standards.

Qualifications

• Strong experience in data engineering with production-grade pipelines and platforms.
• Advanced SQL skills in relational databases, data transformation and modelling.
• Strong Python development, pandas, scalable data-processing workflows.
• Hands-on API and ETL/ELT data integrations.
• Experience in cloud data environments, ideally Microsoft Azure.
• Software engineering discipline with unit/integration/pytest tests.
• Familiarity with CI/CD, GitHub and Azure DevOps workflows.
• Infrastructure-as-Code via Terraform/Terragrunt.
• Interest in AI-enabled data engineering and prompt-based workflows.
• Analytical, troubleshooting and technical planning skills.

Responsibilities

• Build and maintain Burson’s core data platform and pipelines for ingestion, transformation, storage and delivery of intelligence data.
• Design robust ingestion pipelines for media monitoring, social listening, APIs and third-party data sources.
• Develop transformation and data-modelling layers using SQL and Python for client datasets and reporting.
• Deliver client-specific data engineering solutions and custom pipelines.
• Improve performance, scalability and reliability of data systems and pipelines.

Skills

Data engineering

SQL proficiency

Python development

ETL/ELT architectures

Azure cloud

GitHub / CI/CD

AI-enabled data workflows

Agile methodologies

Power BI

Security best practices

Tools

Azure DevOps

Terraform

Terragrunt

GitHub Actions

Job description

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 PowerBI.
• 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.

What we offer:

• The opportunity to work at one of the largest and most awarded PR agencies in Poland and worldwide
• A hybrid working model
• Access to a suite of generative AI tools
• Fully funded private healthcare package for employees
• A benefits platform with a flexible cafeteria system (including Medicover Sport)
• A clear career path and opportunities for professional development through both global and local training programs.
• EAP