Data Infrastructure & MLOps Engineer (all genders)

Data Infrastructure & MLOps Engineer (all genders) Build the reliable data and ML platforms behind Doodle’s products, shaping scalable infrastructure, automation, and responsible machine learning used by millions. Apply Now We usually respond within two weeks Build the Data and Machine Learning Foundation Behind Every Doodle Product Doodle is a B2B SaaS platform used by millions of professionals to coordinate meetings and collaboration. Reliable data infrastructure and well operated machine learning systems are essential to building secure, intelligent, and scalable products for our customers. 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 maintain. What You Will Do As a Data Infrastructure & MLOps Engineer, you will own the reliability, scalability, and automation of Doodle's data and machine learning platforms. Data Infrastructure & Platform Engineering • Design, build, and operate scalable data infrastructure for ingestion, transformation, storage, and serving. • Develop reliable batch and streaming data pipelines that support product analytics, business intelligence, and machine learning use cases. • Establish data platform standards for performance, availability, observability, documentation, and cost management. • Improve 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 rollback. • Operate machine learning workloads in production, including model serving, feature pipelines, scheduled retraining, and inference infrastructure. • Partner with data scientists and software engineers to turn prototypes into reliable, maintainable production services. • Introduce repeatable approaches for model validation, monitoring, drift detection, performance measurement, and incident response. Cloud Infrastructure & Automation • Manage cloud-based data and machine learning infrastructure using infrastructure as code and automated deployment practices. • Build secure, reproducible environments for development, testing, and production. • Improve platform efficiency through automation, capacity planning, resource optimisation, and sensible cost controls. • Contribute to platform architecture decisions and help evolve Doodle's technical foundations as the business grows. Reliability, Security & Governance • Define and maintain service level objectives, operational runbooks, alerts, dashboards, and on-call processes for critical data and ML systems. • Protect sensitive data through appropriate access controls, encryption, secrets management, retention policies, and secure development practices. • Support compliance, privacy, and responsible AI requirements by making data and model operations traceable, auditable, and well documented. • Investigate incidents, lead root cause analysis, and implement preventative improvements across the platform. Collaboration & Enablement • Work with product, engineering, analytics, data science, security, and operations teams to understand requirements and deliver practical platform solutions. • Create clear documentation, reusable tooling, and self-service workflows that enable teams to work independently. • Contribute to engineering standards, technical planning, code reviews, and knowledge sharing. What You Bring Professional experience in data engineering, platform engineering, MLOps, DevOps, or a closely related role. Strong Python and SQL skills, with experience developing production-quality software and data pipelines. Hands-on experience with cloud infrastructure, containers, CI/CD, and infrastructure as code. Experience with data warehouses, data lakes, workflow orchestration, and batch or streaming processing. Practical knowledge of machine learning lifecycle management, model deployment, monitoring, and reproducibility. Experience with observability, incident management, system reliability, and performance optimisation. A security-conscious approach to data access, privacy, secrets management, and production operations. Strong communication skills and the ability to explain technical decisions to both technical and non-technical stakeholders. Nice to Have Experience with tools such as Kubernetes, Terraform, Airflow, dbt, Spark, Kafka, MLflow, or similar technologies. Experience operating machine learning systems in a B2B SaaS or high-growth technology environment. Knowledge of feature stores, vector databases, LLM applications, retrieval-augmented generation, or agentic AI systems. Experience implementing data quality frameworks, lineage, governance, and privacy controls. Experience supporting ISO 27001, SOC 2, GDPR, or other security and compliance programmes. Interest in building simple, scalable platforms that reduce operational complexity for other teams. Hiring Journey Initial Application Review + BRYQ Assessment Hiring Manager Interview Technical Assessment Cross Functional Technical Interview Executive Interview + HR Interview So, Get in Touch! At Doodle, we are committed to providing an environment of mutual trust and respect, where equal employment opportunities are available to all applicants and teammates without regard to age, race, colour, disability, religion, gender, or sexual orientation. Diversity and inclusion are important to us because the best products are built by teams with different experiences, perspectives, and backgrounds. IMPORTANT NOTICE: Please note that we can only consider your application if you are based and have the right to work in Germany. At this time, we are unable to sponsor visas for this position or support relocation. Department Engineering Locations Berlin Remote status Hybrid Team & Culture We're a tight-knit team of 100+ spread out over four different countries. We’ve got our home-base in Zurich and awesome offices in Berlin and Belgrade. We also have colleagues working remotely from the USA. We’re not a huge company so our team structure enables us to quickly implement new ideas before they get stale on the corporate ladder. Everyone here is encouraged to contribute or experiment, and if we’re in agreement about a new decision we go for it.