Summary
Build reliable data infrastructure, deploy machine learning models, automate cloud environments, and improve observability across scalable production systems.
Highlights
Work on production data platforms, collaborate with data teams, use modern cloud tooling, and benefit from learning opportunities and flexible work.
Description
Senior DataOps Engineer / Software Engineer
Location: Munich, hybrid
About the company:
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.
For hosts looking to grow their business and guests looking for their perfect stay.
Data is at the core of everything they do.
The Team:
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.
The tech stack
Data Pipelines: Airflow + dbt-core, powered by PythonStorage & Querying: S3, Redshift, Athena, DuckDBML & Model Serving: MLflow, SageMaker, deployment APIsCloud & DevOps: Terraform, Docker, Jenkins, AWS EKS (Kubernetes)Monitoring: ELK, Grafana, Looker, OpsGenieIngestion: Kafka, Airbyte, FivetranAI tooling: Claude, Copilot, Codex
What you'll do
Support model deployment and serving, help bring pricing and demand models into production, build and maintain APIs and serving infrastructureBuild and operate production pipelines, ensuring data flows are reliable from source to model to output with proper monitoring and alertingCollaborate cross-functionally with Data Scientists, Analysts, and Engineering teams to turn prototypes into production-ready solutionsOwn infrastructure and tooling, set up and maintain environments, CI/CD pipelines, and the infrastructure the team depends onEnsure operational excellence through monitoring, automated testing, and observability across production systemsMigrate and productionize POCs: turn experimental code into robust, maintainable Python applicationsMaintain data quality, consistency, and documentation across revenue management metrics and datasets
What you bring
4+ years of experience in Software Engineering, Data Engineering, DevOps, or MLOpsStrong Python skills: you write clean, production-quality codeHands-on experience with CI/CD, Docker, and infrastructure-as-code (Terraform or similar)Familiarity with cloud platforms (AWS preferred) and deploying services in productionExposure to or interest in ML model deployment (MLflow, SageMaker, or similar) is a strong plusCuriosity about cutting-edge LLM tools and agents to boost productivityA proactive, ownership-driven mindset: you spot problems and drive solutions forward
What's in it for you
Impact: work on products used by millions, where data drives decisions and your contributions are visibleGrowth: a culture built on curiosity and feedback, with personal learning budgets and a strong AI focusGreat people: smart, international colleagues who challenge and support each other in equal measureTech environment: scale-up pace with a proven business model; you build, test, and iterate continuouslyFlexibility: hybrid setup (50% in-office), plus up to 8 weeks/year working from other locationsPerks: travel benefits, gym discounts, and regular team events across nearly 30 offices
