Senior Data/ML Engineer (all genders) at Eurowings Digital GmbH

Cologne, North Rhine-Westphalia, Germany -

Full Time

Start Date

Immediate

Expiry Date

17 Sep, 26

Salary

0.0

Posted On

19 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Scala, Python, SQL, Spark, Azure Cloud Platform, Databricks, Kafka, Azure DevOps, GitHub, Terraform, ETL/ELT, MLOps, Data Modeling, CI/CD, Distributed Processing, Data Pipeline Orchestration

Industry

Information Technology & Services

Description

Job overview We are looking for a versatile, senior Data / ML Engineer to join us. You will sit at the critical intersection of Data Engineering and Machine Learning, serving as the bridge between raw data infrastructure and production-ready AI models. This role requires a pragmatic engineering mindset. You will be responsible for architecting robust data pipelines, designing scalable transformation logic, and building the MLOps infrastructure required to deploy, monitor, and scale machine learning workflows in production. What you will do Data Infrastructure & Pipelines: Design, build, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) to ingest and process large-scale structured and unstructured data using Spark and cloud-native architectures ML Systems & MLOps: Collaborate closely with Data Scientists to transition experimental models into clean, production-ready code and robust pipelines Analytics Engineering: Implement advanced data modeling and transformation logic to ensure high-fidelity inputs for both downstream models and business analytics Operational Excellence: Build continuous integration and deployment (CI/CD) pipelines for data and ML workflows, ensuring system reliability, data quality, and uptime Technical Stack: Languages & Frameworks: Scala, Python, SQL, Spark (with experience using Spark in production environments) Cloud & Infra: Azure Cloud Platform, Databricks, Kafka CI/CD & IaC: Azure DevOps / GitHub, Terraform Job Title Senior Data/ML Engineer Job Subtitle Job Introduction What you will need Experience: 7+ years of experience in data engineering, ideally with hands-on exposure to analytics engineering practices (e.g., data modeling, transformation logic) Data Science Partnership: Proven experience working closely with data scientists or driving data science projects with a highly pragmatic, production-focused mindset Core Engineering: Deep understanding of data pipeline orchestration, distributed processing, and building resilient, testable ETL/ELT systems Data Modeling: Solid grasp of data modeling concepts, especially in the context of analytics and reporting (conceptual, logical, and physical models) Streaming: Familiarity with streaming data frameworks (Kafka, Event Hubs, or similar) What you will bring Excellent Communication: Ability to explain complex technical concepts to both technical and non-technical stakeholders Collaboration Mindset: Strong ability to work effectively with cross-functional teams including data science, engineering, and business units Stakeholder Management: Skilled in balancing immediate business needs with long-term technical feasibility Quality Focus: High attention to detail with a strong focus on data quality, accuracy, and reliability Execution: A self-starter with strong organizational skills and the ability to drive initiatives from concept to completion Eurowings Digital Benefits Inclusivity all genders Workplace type Hybrid Apply now! About us We are dreamers, doers, and enthusiasts! Our mission is to enable our customers on leisure and/or business travel to enjoy a seamless travel booking experience from the tip of their fingers. Therefore, we are continuously working passionately on providing diverse and attractive offers on our online booking platforms Eurowings & Eurowings Holidays and mobile Apps to empower our customers with relevant information and smart digital services throughout their travel experience.

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Responsibilities

Architect and maintain scalable data pipelines and MLOps infrastructure to bridge raw data and production AI models. Collaborate with data scientists to transition experimental models into production-ready code and ensure high-fidelity data inputs.