Top Data Engineer Jobs in Munich

Build and maintain SQL and Postgres databases and data pipelines for acoustic sensor and flight-test data. Structure, clean, and manage large field-collected datasets, create tools for data accessibility, and convert raw test results into actionable datasets. Collaborate with hardware, flight-test, and machine-learning teams to ensure data quality from collection through analysis.

Supports customer-facing deployment projects by gathering and analyzing data, directing AI agents to build models and dashboards, configuring Celonis solutions, validating outputs, developing KPIs, and documenting solution designs. The intern works alongside experienced Deployment Engineers while learning AI-augmented software delivery, business process analysis, specification writing, and context engineering.

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.

Build the company’s greenfield data foundation, including its warehouse, resilient ETL/ELT pipelines, governance, quality controls, and security for sensitive financial data. Select cloud and orchestration technologies, integrate complex third-party APIs, and support AI-driven finance products. The role offers opportunities to hire and mentor engineers and grow into leadership.

Designs and builds scalable enterprise data pipelines, integrations, and lakehouse infrastructure connecting SAP, Siemens Teamcenter, engineering, manufacturing, and operational systems. Responsibilities include data modeling, ETL/ELT, orchestration, quality validation, metadata and lineage, monitoring, governance, and reusable data services. The role requires hands-on technical ownership, collaboration with enterprise stakeholders and software engineers, and establishing engineering standards for reliable analytics, AI/ML, automation, and application data access.

Implement and improve the data centre health and safety management system. Conduct risk assessments, RAMS reviews, inspections, audits, incident investigations, and contractor safety activities. Deliver training and toolbox talks, monitor safety KPIs, maintain emergency response plans, and ensure compliance with legislation, standards, and company policies. The role requires regular site presence and occasional travel between sites.

Support and maintain data pipelines in Microsoft Fabric, onboard data sources, transform and validate data across Bronze, Silver, and Gold layers, and maintain datasets for reporting, analytics, and AI initiatives. Collaborate with engineers, analysts, and stakeholders, monitor processing, resolve issues, document processes, and contribute to data platform modernization and continuous improvement.

Supports and maintains enterprise data processes across Google Cloud Platform and Microsoft Fabric. Responsibilities include monitoring and validating data flows, performing data quality and reconciliation checks, troubleshooting inconsistencies, maintaining ETL/ELT pipelines, onboarding integrations, documenting processes, and improving the data platform. The role collaborates with data engineering, infrastructure, analytics, and business teams to ensure secure, accurate, and trusted data for reporting and decision-making.

Develop, maintain, and support web-based applications, APIs, and core data infrastructure for FWW. Implement new data layers, visualization tools, automations, and provide cross-departmental technical support.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Lead the architecture and development of Core Data Services for customer-facing applications on SAP BTP and SAP RISE. Design modular services using RAP and CAP, integrate through SAP BTP Integration Suite, and implement authentication and authorization with XSUAA. Provide technical leadership through architecture decisions, code reviews, mentoring, release governance, documentation, and deployment readiness. Contribute to AI-enabled solutions and deliver secure, scalable applications for regulated financial services environments.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Own and optimize high-throughput batch and streaming data pipelines, distributed Apache HBase storage, and production infrastructure for maritime telemetry and predictive analytics. Operate containerized AWS environments using Kubernetes, Docker, Terraform, and GitOps; establish reliability metrics and operational SLAs. Collaborate with data science, product, and SRE teams, mentor engineers, and promote strong code review and AI-assisted development practices.

Zuhlke is seeking an Expert Data Engineer focused on Databricks to support client transformation initiatives. The provided description contains limited role-specific responsibilities, qualifications, or project details. The position appears aligned with data engineering and the development of modern data solutions within Zuhlke’s multidisciplinary engineering and innovation teams.

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 scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

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.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Design, develop, and operate scalable data and backend systems for ingesting, processing, storing, and serving engineering and operational data. Build APIs, pipelines, data products, and ETL/ELT processes; contribute to data architecture, quality, governance, observability, and documentation. Collaborate cross-functionally and mentor less experienced engineers while working with streaming, time-series, analytical storage, cloud data platforms, and event-driven architectures.

Lead and operate production-grade data pipelines and streaming systems to ingest, enrich, process, and serve web event and intent data at scale. Own cloud infrastructure as code, observability, data quality, and CI/CD. Collaborate with product and ML teams to deliver reliable, low-latency data powering customer-facing features and AI/ML workflows.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Own and operate the ClickHouse data warehouse and ETL pipelines; build and maintain dashboards and BI layer; provide proactive analyses (cohorts, unit economics, attribution); implement monitoring/alerting; work closely with co-founders and cross-functional leaders to make data the single source of truth.

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