Top Data Engineer Jobs in Munich

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.

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.

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 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.

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.

The Software Engineer will enhance data collection processes for Speechify's AI team, managing the ingestion pipeline and collaborating on dataset roadmaps.

Design, develop, and optimize GPU- and CPU-accelerated image and data processing libraries. Implement and performance-tune parallel numerical algorithms, benchmark across architectures, contribute to technical roadmaps, provide technical leadership, and collaborate with product and engineering teams to meet feature and performance requirements.

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

Lead architecture and development of scalable simulation, validation, and data platforms for autonomous robotics. Build fleet data collection, telemetry pipelines, and cloud-native ingestion/processing systems. Architect SiL/HiL and large-scale virtual testing frameworks, automate validation pipelines, and drive system-level verification. Provide technical leadership across robotics, autonomy, embedded, and platform teams, mentor engineers, and support field integration and operational readiness.

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.

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.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

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.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design, build and maintain Zero-ETL scalable data pipelines and Redshift data models. Develop and manage Airflow workflows, integrate multiple data sources, ensure data quality and availability, optimize queries and warehouse performance, monitor and resolve platform issues, document pipelines and collaborate with analysts and stakeholders on architecture and analytics needs.