> Markdown version of [/jobs/ext/2041812-expert-data-scientist](https://www.wearedevelopers.com/jobs/ext/2041812-expert-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Expert Data Scientist - **Company:** Nespresso Deutschland GmbH - **Location:** Frankfurt am Main, Germany - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Java (Programming Language), Agile Methodology, Artificial Intelligence, Airflow, Data Analysis, Audit Trail, Automation of Tests, Microsoft Azure, Cloud Database, Cloud Storage, Information Systems, System Configuration, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, Distributed Computing Environment, Distributed Data Store, Distributed Systems, Interoperability, Python (Programming Language), Key Management, Metadata, Performance Tuning, Reliability Engineering, Cloud Services, DataOps, Search Technologies, Software Engineering, SQL Databases, Data Streaming, Unstructured Data, Feature Engineering, Data Ingestion, Azure Data Factory, Snowflake, Apache Spark, Change Data Capture, Git, Microsoft Fabric, Data Lakes, Core Data, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Data Analytics, Data Management, Machine Learning Operations, Terraform, Stream Processing, Azure Synapse Analytics, Data Pipelines, Serverless Computing, Databricks, Programming Languages - **Published:** August 13, 2026 - **Apply:** https://www.adzuna.de/details/5840262918 ## About the Role * Bachelor's degree in Computer Science, Engineering, Information Systems, Data Management, Data Analytics, or a related field; equivalent experience will be considered. * 8+ years of experience in data engineering, data platform engineering, cloud data platforms, software engineering for data systems, or related technology roles. * Expertise building and optimizing scalable distributed data systems across platforms such as Databricks, Microsoft Azure, Spark, SQL-based platforms, cloud storage, and cloud-native data services. * Advanced proficiency with SQL and a modern programming language such as Python, Scala, or Java, with strong software engineering practices for reusable, testable, and maintainable code. * Deep experience designing and operating data lakes, lakehouses, data warehouses, hybrid architectures, data pipelines, orchestration frameworks, and enterprise-scale ingestion and transformation patterns. * Strong hands-on experience with batch, streaming, and near-real-time data processing; ETL/ELT frameworks; structured, semi-structured, and unstructured data integration; and high-volume data movement patterns. * Strong knowledge of CI/CD, Infrastructure-as-Code, automation, monitoring, alerting, observability, incident response, root-cause analysis, environment management, and operational excellence practices. * Strong communication, problem-solving, documentation, stakeholder management, and cross-functional collaboration skills, with fluency in English. * Proactive, agile, and self-sufficient mindset, with the ability to work effectively as an expert technical contributor in a dynamic environment. Preferred Qualifications * Experience with Snowflake capabilities such as warehouse optimization, secure data sharing, role-based access, performance tuning, storage and compute management, and governed data consumption patterns. * Experience with medallion/lakehouse architectures, data contracts, schema evolution, change data capture, event-driven pipelines, data observability, and reliability engineering for enterprise data systems. * Experience with orchestration and transformation tools such as Airflow, dbt, Azure Data Factory, Databricks Workflows, Informatica, or equivalent enterprise data integration tools. * Experience with Terraform or similar Infrastructure-as-Code tools, Git-based development, automated testing, deployment automation, secrets management, and platform configuration management. * Exposure to AI/ML enablement, feature engineering, feature stores, vector search, MLOps patterns, and data platform capabilities that support advanced analytics and GenAI use cases. * Certifications such as Azure Data Engineer, Databricks Data Engineer, Snowflake SnowPro, Microsoft Fabric Analytics Engineer, or related cloud/data platform credentials are a plus. ## Description The role: The Expert Data Scientist (Platform Engineer) will be responsible for building, maintaining, and optimizing core data platform capabilities that enable scalable data ingestion, processing, storage, and access across tools such as Databricks, Azure, and Snowflake. This role develops reusable pipelines, frameworks, automation, and platform patterns that ensure data is reliable, performant, secure, cost-effective, and aligned with enterprise standards for governance, security, quality, and interoperability. The role will operate as an expert technical contributor, partnering closely with business, analytics, data product, engineering, architecture, security, platform, and regional/global IT stakeholders to strengthen the data foundation for BI, advanced analytics, AI/ML, and enterprise data products. What you'll do: As an Expert Data Scientist (Platform Engineer) you will: * Design, build, maintain, and optimize core enterprise data platform capabilities that support scalable data ingestion, processing, storage, transformation, governance, and access across Azure Datalake, Databricks, Snowflake, and related cloud data services. * Develop reusable data pipelines, engineering frameworks, platform services, templates, automation scripts, and standard patterns that accelerate delivery while reducing operational friction, duplication, and technical risk. * Architect and implement high-throughput batch, streaming, and near-real-time data processing capabilities using modern data engineering patterns, distributed processing frameworks, orchestration tools, and cloud-native services. * Optimize Databricks, Snowflake, Azure Synapse, and related platform components for performance, reliability, scalability, cost efficiency, observability, and operational resilience. * Embed governance, security, quality, lineage, metadata, access controls, auditability, and compliance requirements into platform capabilities and data engineering patterns by design. * Build and maintain CI/CD, Infrastructure-as-Code, automated testing, monitoring, alerting, deployment, and environment management practices for data platform components and configurations. * Partner with data engineers, analytics engineers, data product teams, architects, platform teams, and business stakeholders to translate data needs into scalable, reusable, and supportable platform capabilities. * Troubleshoot complex data pipeline, platform, integration, access, compute, storage, performance, and reliability issues; lead root-cause analysis and drive durable remediation. * Create documentation, reference implementations, runbooks, standards, and enablement materials that improve developer productivity, self-service adoption, and consistent use of platform capabilities. * Stay current with modern data platform engineering practices, including lakehouse architectures, distributed systems, data observability, data contracts, metadata-driven automation, FinOps, MLOps enablement, and platform-as-product operating models. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Data Analyst Salary Germany](https://www.wearedevelopers.com/magazine/277-data-analyst-salary-germany) - [Backend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/196-backend-developer-salary-in-germany-2023) - [Frontend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/195-frontend-developer-salary-in-germany-2023) - [Fullstack developer salary in Germany [2023]](https://www.wearedevelopers.com/magazine/197-fullstack-developer-salary-in-germany-2023) - [Software Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/194-software-developer-salary-in-germany-2023)