Top Data Engineer Jobs in Munich

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.

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.

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.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

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.

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.

Lead the architecture and development of a multi-country Snowflake and GCP data platform. Build CDC ingestion pipelines, dbt semantic models, and DataOps CI/CD workflows; implement governance, RBAC, PII masking, compliance, FinOps, and performance optimization. Partner with business stakeholders, document architectural decisions, and mentor data engineers across LATAM operations.

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.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, collaborate with senior engineers, and contribute to reliable AI-driven products. Responsibilities require Python, software engineering, deep learning, databases, Docker, and at least one major deep learning framework. Preferred experience includes RAG pipelines, vector databases, LLM fine-tuning, cloud platforms, and Kaggle competitions.

Leads mechanical engineering delivery for complex data centre projects, designing cooling, ventilation, automation and control, and ancillary systems. Coordinates with electrical disciplines, manages engineers, reviews technical work, develops detailed designs, allocates resources, and maintains project timelines and client relationships. Requires experience with mission-critical systems, data centre environments, sustainable design, building physics, CFD, and digital design tools, along with professional engineering qualifications or progression toward IEng or CEng status.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

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.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

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.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

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.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

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.

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.