Marketing Engineer

Build the technology, intelligence, and automation behind UpCloud’s growth. UpCloud is building the future of European cloud. We’re looking for a Marketing Engineer to help us build an equally ambitious Marketing function: one where trustworthy data informs decisions, predictive models uncover opportunities, and connected systems automate execution. You’ll combine software engineering, data engineering, and data science to help every marketing function perform better. You’ll own our marketing technology stack, connect our platforms, build dashboards and analytical models, and turn complex data into clear recommendations. This is a senior, hands-on role within Marketing, reporting to the CMO. You’ll work directly with marketing specialists to understand their challenges and build the tools, insights, and automation they need, collaborating closely with Data, Product, Engineering, Sales, and Finance. Your mission: engineer a Marketing function that understands what happened, anticipates what comes next, and knows which actions to take. What you’ll own Our marketing technology stack. Own the technical operation and evolution of the platforms Marketing depends on, including configuration, integrations, maintenance, reliability, and costs. Evaluate new tools and make informed build-versus-buy decisions across SaaS and self-hosted open-source solutions. Marketing data pipelines and models. Build and maintain pipelines that bring advertising, website, CRM, social media, product usage, and commercial data together in BigQuery. Develop reusable data models, consistent identifiers, and shared metric definitions. Make data quality, freshness, and traceability part of everyday operations. A deep understanding of customer behavior. Learn how users interact with UpCloud’s control panel and products, including the events and behavioral data available through self-hosted Matomo. Work with Product and Engineering to understand underlying event definitions and improve instrumentation. Connect these signals with acquisition, campaign, CRM, and social data to reveal what drives activation, conversion, adoption, and customer value. BI dashboards and self-service analytics. Design, build, and maintain dashboards in Google Data Studio / Looker Studio. Work directly with internal Marketing stakeholders to translate their questions into useful reporting, analytical datasets, and decision tools. Give each function a dependable view of its performance and the ability to investigate further. Hands-on marketing data science. Personally analyze campaigns, channels, audiences, customer journeys, and experiments. Apply statistics and data science to questions such as which campaigns generate valuable customers, where conversion breaks down, which audiences respond, and where investment could produce better results. Support paid media, lifecycle marketing, SEO, content, events, partners, and brand with appropriate analysis and recommendations. The progression from descriptive to predictive and prescriptive analytics. Build beyond dashboards and historical trends. Train and validate models using historical campaign, customer, product, and external data to forecast outcomes and identify emerging demand patterns. Develop prescriptive approaches that recommend next actions—such as budget allocation, audience priorities, lifecycle interventions, or the next experiment—using predictions, business constraints, and evidence from testing. Measurement and experimentation. Maintain tracking, tagging, campaign taxonomies, and conversion measurement across our websites and control panel. Design experiments and develop approaches to attribution, incrementality, and marketing mix modeling where the data supports them. Validate findings, distinguish correlation from causation, and communicate assumptions and uncertainty clearly. New tools, channels, APIs, and MCP integrations. Continuously evaluate and connect the tools Marketing wants to experiment with. Build integrations through APIs, webhooks, connectors, and Model Context Protocol (MCP), where supported. This may include emerging advertising channels such as ChatGPT Ads, social media platforms, and new research, content, or experimentation tools. Ensure each experiment can feed useful data back into our shared analytics environment. Agentic workflows and process automation. Build agents and workflows that execute recurring marketing processes end to end, from gathering data and preparing campaigns to monitoring, optimization, and reporting. Enable autonomous execution within agreed goals, permissions, and budgets, with evaluation, monitoring, and clear exception handling. Engineering quality and stakeholder delivery. Turn stakeholder needs into a prioritized roadmap tied to business outcomes. Apply version control, testing, dependable deployment practices, monitoring, and documentation. Train colleagues to use the systems you build, and continuously improve their usefulness, reliability, and adoption