Top Data Engineer Jobs in Munich

The role involves analyzing data for insights, creating dashboards, and collaborating with teams to optimize product performance using SQL, Python, and visualization tools.

Build auditable cloud data pipelines and perform statistical analyses on clinical and medical imaging datasets. Apply classical ML, deep learning, and LLM methods to extract and analyze clinical data, maintain data lineage and reproducibility, create dashboards and observability tooling, and produce evidence suitable for regulatory submissions and audits.

Build, optimize, and maintain data pipelines and data systems using Python and PySpark. Evaluate and annotate AI-generated ETL outputs, ensure data accuracy and scalability, integrate data solutions with software teams, document processes, follow data governance, and support backlog and best practices under senior guidance.

The Finance Data Engineer will design and deploy automated pipelines, ensure finance data integrity, and implement AI-driven solutions while maintaining data sovereignty and compliance with government contracts.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Lead development and scaling of data processing pipelines for road and navigation products. Design and implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate within a cross-functional team to build high-quality, distributed data systems supporting mapping and navigation features.

Design, build, and operate governed data pipelines and context layers for AI agents integrating Salesforce, SAP, and service data. Implement feedback loops, data-quality frameworks, semantic models, Power BI reporting, and secure API-based integrations to support enterprise commercial AI workflows.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and operate scalable AWS-based data pipelines and a lakehouse. Clean and model structured/unstructured data for reporting, analytics, and AI. Implement testing and monitoring for pipeline reliability. Collaborate with product and engineering teams and share data engineering best practices across the company.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Responsible for deploying and maintaining data center infrastructure, troubleshooting hardware and network issues, managing inventory, and setting up data centers.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Design, build, and operate scalable data systems and ETL pipelines for research and live trading. Transform raw data into reliable datasets, collaborate with researchers, manage storage systems, and maintain production observability and performance.

As a Software Engineer, analyze and process real-time athlete data to enhance performance and reduce injury risks, developing algorithms and optimizing data pipelines.

The Technical Privacy Expert will implement privacy-by-design principles, ensure compliance with regulations, mentor teams, and develop technical frameworks for privacy in technologies.

Design and deliver scalable data pipelines for model development and production ML workflows in autonomous driving. Ingest, transform, curate, validate, and monitor large-scale real-world, synthetic, and partner datasets. Improve pipeline performance and collaborate with ML engineers, Data Corpus, AI Platform, and partners to ensure datasets are reliable, traceable, and model-ready.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

The role involves data collection to support AI model training, managing cloud infrastructure on GCP, and improving data ingestion processes.

Join the AI & Biometrics team to develop a resilient data platform for machine learning, focusing on data ingestion, transformation, and secure API development.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and scale low-latency market data systems and public APIs. Influence architecture, implement backend services, maintain quality and tests, participate in reviews, troubleshoot incidents, and optionally serve on-call.