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.

• 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.

• 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.

• 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 (budget allocation, audience priorities, lifecycle interventions, or the next experiment) using predictions, business constraints, and evidence from testing.

• 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.

Our environment combines SaaS and self-hosted platforms:

• Data and business intelligence: BigQuery and Google Data Studio / Looker Studio.

• Web, search, and product analytics: GA4, Google Tag Manager, Google Search Console, and self-hosted Matomo for control panel analytics.

• Consent management: iubenda.

• CRM and lifecycle automation: HubSpot.

• Website and SEO: WordPress and Semrush.

• Financial planning and reporting: Abacum, in collaboration with Finance.

• Advertising and social data: Sources including LinkedIn, X, Facebook, and other platforms used by Marketing.

• Emerging tools: New advertising channels, AI applications, experimentation platforms, and available APIs and MCP integrations.

You’ll help shape how these systems work together and what comes next. We value open-source technology and self-hosting where they offer meaningful advantages in control, flexibility, and total cost of ownership, balanced against delivery speed, reliability, and maintenance effort.

• Strong software and data engineering skills. You write production-quality Python and SQL, work confidently with APIs and authentication, and build dependable integrations and pipelines. You’re comfortable using JavaScript for web tracking and integrations.

• Experience connecting complex datasets. You can bring together behavioral, campaign, CRM, and commercial data at appropriate user, account, campaign, or cohort levels. You know how to investigate missing data, duplicates, inconsistent definitions, and misleading joins.

• Hands-on BI expertise. You’ve built dashboards and analytical tools in Google Data Studio / Looker Studio or a comparable platform. You understand how to make reporting useful for people who need to make decisions.

• The ability to turn analysis into action. You work with stakeholders to clarify the decision behind a request, choose the right analytical approach, and deliver recommendations they can use. You’re comfortable explaining uncertainty and challenging conclusions the data does not support.

• Experience shipping automation. You’ve built workflows or internal tools that reduce recurring work, and can explain how you tested them, monitored their performance, and handled failures.

• Independent ownership and clear communication. You can prioritize competing needs, take a problem from discovery through implementation, and explain technical choices to colleagues across disciplines.

• Marketing mix modeling, causal inference, forecasting, optimization, or recommendation systems.

• Building and operating AI agents, LLM applications, or MCP integrations.

• Operating self-hosted open-source applications, including Linux, containers, and deployment automation.

• Working in B2B SaaS, cloud infrastructure, developer-focused products, or product-led growth.

You don’t need experience with every platform in our stack. We’re interested in your ability to build, analyze, learn, and deliver measurable improvements.

Marketing stakeholders have reliable dashboards, clear answers, and recommendations they can act on. Campaign and product data connect into a coherent view of customer behavior. Predictive models demonstrate useful performance against real outcomes, and prescriptive recommendations are tested for business impact.

Recurring work increasingly runs through dependable automation. Specialists can investigate performance and execute repeatable processes independently. Your impact shows up in faster decisions, reduced manual effort, stronger measurement, and better marketing results.

You’ll shape the technology and analytical capabilities of an entire Marketing function, with direct access to the CMO and close collaboration with technical and commercial teams.

This is an opportunity to build systems that influence how we understand customers, invest in growth, and operate every day.

If you enjoy connecting engineering and data science with business outcomes, we’d love to hear from you.

The pay range for this role is around 5000 - 6000 euros per month, depending on your seniority and location.

We review all applications and get back to all our candidates - that is, a human, not an AI. That is a promise.

• Up to six weeks of annual paid vacation

• Flexible and remote-friendly culture

• Occupational healthcare

• Travel insurance

• Sports benefit

• Company events and activities

• Share Appreciation Rights program

• Udemy E-learning platform and budget for trainings; unlimited business access

• Annual company trip