Title: Data Platform Engineer

Level: Mid-Level Engineer, Data & Technology Solutions

Location: Copenhagen

Reporting to: Lead Engineer

About Open Intelligence:

We are the Activation arm of WPP Open Intelligence. Our team builds the foundational data infrastructure and high-performance edge services that power WPP’s position in the market. With approximately one-third of all global media spend flowing through WPP, our platform operates at the core of this massive network—deeply integrated and adopted by the largest supply-side partners in the AdTech industry. Operating at our scale presents unique engineering challenges. Our edge services currently handle over 60,000 classification requests per second, while our contextual classification pipelines process 10,000 requests per second. With operations spanning the US, UK, and ongoing expansion into EMEA and APAC, our infrastructure continuously interacts with up to 98% of the population in our active markets. We are an engineering-led team focused on building robust, scalable, and highly reliable systems.

WHO WE ARE LOOKING FOR

We are seeking a skilled Data Platform Engineer to design, build, evolve, and optimize our cloud infrastructure, CI/CD pipelines, and deployment processes for our global data and AI platform. You will collaborate closely with data scientists, data engineers, QA, and IT teams to streamline development workflows, improve system reliability, and ensure secure, scalable infrastructure. This is a hands-on technical role for someone who is comfortable delivering reliable data solutions and is eager to leverage new development and testing technologies. You will collaborate closely with team members and product stakeholders to ensure our data infrastructure meets evolving business needs.

WHAT YOU WILL DO:

· Design scalable cloud-native systems on platforms such as Amazon Web Services or Google Cloud Platform that support Spark data analytics and AI workloads

· Implement Infrastructure as Code using HashiCorp Terraform

· Build secure multi-account / cross-cloud environments

· Optimize cost efficiency for high-scale data workloads and low latency microservices

· Enable visibility on performance and cost metrics per environment and workload

· Deploy and manage clusters with Kubernetes with containerized data workloads and microservices

· Implement blue/green and canary deployment strategies

· Build robust automations using tools such as GitHub Actions, GitLab CI/CD, or Argo Workflows

· Automate testing, security scans, and compliance checks

· Assist other team members in architectural choice of cloud services

· Enforce a small, coherent and flexible configuration surface to reduce deployment time and improve release confidence, in collaboration with other team members

· Champion DRY and DevOps principles across the team through code reviews, documentation, and shared tooling

WHAT YOU WILL NEED

5+ years of experience in Data Platform Engineering, Cloud Engineering, DataOps or Site Reliability Engineering (SRE) within enterprise-scale environments

Strong hands-on experience with cloud platforms such as Amazon Web Services or Google Cloud Platform

Proven expertise in Infrastructure as Code using HashiCorp (or equivalent IaC tooling)

Deep experience containerizing and orchestrating applications using Docker and Kubernetes

· Strong experience building and maintaining CI/CD pipelines

· Solid scripting skills (Bash, Python, or similar) with a strong automation-first mindset

· Golang knowledge is a plus

· Experience implementing observability and monitoring solutions such as Datadog, Prometheus, or the Elastic stack

· Strong understanding of networking concepts (VPCs, load balancing, DNS, IAM, security groups)

· Experience working with data-intensive, AI/ML, or marketing technology platforms at scale

· Knowledge of security principles, security best practices, and compliance standards (e.g., GDPR)

· Experience supporting multi-region, globally distributed systems

· Strong collaboration and communication skills, with the ability to partner across engineering, product, and data teams

· A proactive, ownership-driven mindset with a focus on reliability, scalability, and continuous improvement