<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Senior DataOps Engineer / Software Engineer </strong></p><p>Location: Munich, hybrid</p><p><br/></p><p><strong>About the company:</strong></p><p>Our client is one of Europe's fastest-growing travel tech scale-ups, connecting millions of guests with holiday homes across the globe. With a team of 700+ people from 60+ countries, they move fast, think big, and build tech that genuinely matters. </p><p>For hosts looking to grow their business and guests looking for their perfect stay. </p><p>Data is at the core of everything they do.</p><p><br/></p><p><strong>The Team:</strong></p><p>You'll join our newly formed Dynamic Pricing and Revenue Management team, working alongside a Data Scientist and a Data Analyst. Your shared mission: help hosts improve occupancy and earnings through smart, data-driven pricing strategies. A cross-functional setup, modern tooling, and teammates who care about impact and learning together.</p><p><br/></p><p><strong>The tech stack</strong></p><ul><li><strong>Data Pipelines:</strong> Airflow + dbt-core, powered by Python</li><li><strong>Storage &amp; Querying:</strong> S3, Redshift, Athena, DuckDB</li><li><strong>ML &amp; Model Serving:</strong> MLflow, SageMaker, deployment APIs</li><li><strong>Cloud &amp; DevOps:</strong> Terraform, Docker, Jenkins, AWS EKS (Kubernetes)</li><li><strong>Monitoring:</strong> ELK, Grafana, Looker, OpsGenie</li><li><strong>Ingestion:</strong> Kafka, Airbyte, Fivetran</li><li><strong>AI tooling:</strong> Claude, Copilot, Codex</li></ul><p><br/></p><p><strong>What you'll do</strong></p><ul><li>Support model deployment and serving, help bring pricing and demand models into production, build and maintain APIs and serving infrastructure</li><li>Build and operate production pipelines, ensuring data flows are reliable from source to model to output with proper monitoring and alerting</li><li>Collaborate cross-functionally with Data Scientists, Analysts, and Engineering teams to turn prototypes into production-ready solutions</li><li>Own infrastructure and tooling, set up and maintain environments, CI/CD pipelines, and the infrastructure the team depends on</li><li>Ensure operational excellence through monitoring, automated testing, and observability across production systems</li><li>Migrate and productionize POCs: turn experimental code into robust, maintainable Python applications</li><li>Maintain data quality, consistency, and documentation across revenue management metrics and datasets</li></ul><p><br/></p><p><strong>What you bring</strong></p><p><br/></p><ul><li>4+ years of experience in Software Engineering, Data Engineering, DevOps, or MLOps</li><li>Strong Python skills: you write clean, production-quality code</li><li>Hands-on experience with CI/CD, Docker, and infrastructure-as-code (Terraform or similar)</li><li>Familiarity with cloud platforms (AWS preferred) and deploying services in production</li><li>Exposure to or interest in ML model deployment (MLflow, SageMaker, or similar) is a strong plus</li><li>Curiosity about cutting-edge LLM tools and agents to boost productivity</li><li>A proactive, ownership-driven mindset: you spot problems and drive solutions forward</li></ul><p><br/></p><p><strong>What's in it for you</strong></p><ul><li><strong>Impact: </strong>work on products used by millions, where data drives decisions and your contributions are visible</li><li><strong>Growth:</strong> a culture built on curiosity and feedback, with personal learning budgets and a strong AI focus</li><li><strong>Great people:</strong> smart, international colleagues who challenge and support each other in equal measure</li><li><strong>Tech environment: </strong>scale-up pace with a proven business model; you build, test, and iterate continuously</li><li><strong>Flexibility:</strong> hybrid setup (50% in-office), plus up to 8 weeks/year working from other locations</li><li><strong>Perks:</strong> travel benefits, gym discounts, and regular team events across nearly 30 offices</li></ul><p></p> </div>