As a Data Infrastructure & MLOps Engineer, you will design, build, and operate the platforms that enable data engineering, analytics, and machine learning across Doodle. Working closely with product, engineering, data, and security teams, you will make data easier to access, models easier to deploy, and systems easier to monitor and maintainAs a Data Infrastructure & MLOps Engineer, you will own the reliability, scalability, and automation of Doodle’s data and machine learning platformsDesign, build, and operate scalable data infrastructure for ingestion, transformation, storage, and servingDevelop reliable batch and streaming data pipelines that support product analytics, business intelligence, and machine learning use casesEstablish data platform standards for performance, availability, observability, documentation, and cost managementImprove data discoverability and usability through data cataloguing, lineage, ownership, and quality processes.
Machine Learning Operations
Build and maintain MLOps workflows covering experimentation, data and model versioning, training, evaluation, deployment, and rollbackOperate machine learning workloads in production, including model serving, feature pipelines, scheduled retraining, and inference infrastructurePartner with data scientists and software engineers to turn prototypes into reliable, maintainable production servicesIntroduce repeatable approaches for model validation, monitoring, drift detection, performance measurement,



and incident responseManage cloud-based data and machine learning infrastructure using infrastructure as code and automated deployment practicesBuild secure, reproducible environments for development, testing, and productionImprove platform efficiency through automation, capacity planning, resource optimisation, and sensible cost controlsContribute to platform architecture decisions and help evolve Doodle’s technical foundations as the business growsDefine and maintain service level objectives, operational runbooks, alerts, dashboards, and on-call processes for critical data and ML systemsProtect sensitive data through appropriate access controls, encryption, secrets management, retention policies, and secure development practicesSupport compliance, privacy, and responsible AI requirements by making data and model operations traceable, auditable, and well documentedInvestigate incidents, lead root cause analysis, and implement preventative improvements across the platformWork with product, engineering, analytics, data science, security, and operations teams to understand requirements and deliver practical platform solutionsCreate clear documentation, reusable tooling, and self-service workflows that enable teams to work independentlyContribute to engineering standards, technical planning, code reviews,



and knowledge sharing
Professional experience in data engineering, platform engineering, MLOps, DevOps, or a closely related roleA security-conscious approach to data access, privacy, secrets management, and production operationsExperience with data warehouses, data lakes, workflow orchestration, and batch or streaming processingPractical knowledge of machine learning lifecycle management, model deployment, monitoring, and reproducibilityExperience with observability, incident management, system reliability, and performance optimisationStrong Python and SQL skills, with experience developing production-quality software and data pipelinesStrong communication skills and the ability to explain technical decisions to both technical and non-technical stakeholdersHands-on experience with cloud infrastructure, containers, CI/CD, and infrastructure as codeExperience with tools such as Kubernetes, Terraform, Airflow, dbt, Spark, Kafka, MLflow, or similar technologiesExperience operating machine learning systems in a B2B SaaS or high-growth technology environmentKnowledge of feature stores, vector databases, LLM applications, retrieval-augmented generation, or agentic AI systemsExperience implementing data quality frameworks, lineage, governance, and privacy controlsExperience supporting ISO 27001, SOC 2, GDPR, or other security and compliance programmesInterest in building simple, scalable platforms that reduce operational complexity for other teams
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📌 Data Infrastructure & MLOps Engineer (Madrid)
🏢 Doodle
📍 Madrid