Top Data Engineer Jobs in Germany

Lead the architecture and implementation of scalable data platforms and AI/ML systems supporting Ericsson’s 5G networks. Own data pipelines, distributed processing, storage, model development, deployment, monitoring, MLOps, governance, and privacy frameworks. Drive technical strategy and design reviews while mentoring engineers and collaborating across teams. The role requires strong Python, C++, Java, Spark, Kafka, API, database, and production AI/ML expertise, with additional value from cloud, containerization, GenAI, and telecom experience.

Graduate AI/ML and Data Engineer working with mentors to build data pipelines, ETL processes, machine learning models, and MLOps capabilities for Ericsson’s 5G networks. Responsibilities include data preprocessing, feature engineering, database work, PySpark processing, data validation, model monitoring, and code reviews. The role provides hands-on experience across the AI and data engineering lifecycle.

Develop and maintain the Data Core backend, SQL translation capabilities, and StarRocks integrations. Responsibilities include debugging query performance and correctness, implementing backend features, writing unit and integration tests, participating in design and code reviews, and contributing to technical documentation. Senior engineers lead projects end to end and mentor newer teammates.

Build a reusable Python ingestion framework to replace legacy Java ETL, develop real-time PostgreSQL-to-Snowflake streaming and CDC pipelines, and improve customer onboarding with LLM-assisted schema mapping. Own production deployment, monitoring, data quality, recovery, and secure handling of sensitive data. Collaborate with the Data and AI Platform team to design infrastructure and make pragmatic architectural decisions.

Own and improve production batch data pipelines and data products supporting search, recommendations, analytics, and machine learning workflows. Build Python workflows with Airflow and dbt, optimize BigQuery workloads, maintain data quality checks, alerts, testing, SLOs, and runbooks, and troubleshoot production issues. Develop self-service tooling for analysts and data scientists while collaborating closely with technical and business stakeholders.

Connect and standardize bioprocess data across pilot plants and systems; integrate sensors, equipment, and software; build Python and SQL pipelines, calculations, reports, and Power BI dashboards; and support digital twins, kinetic models, simulations, automation, and digitalization strategy for fermentation process optimization and scale-up.

Build, deploy, and maintain scalable data platforms and pipelines for mission-driven clients. Design enterprise data applications, create software to retrieve and process structured and unstructured data, and develop ETL/ELT workflows for reporting and analytics. Collaborate with multidisciplinary teams to assess, monitor, operate, and scale data platforms in an agile environment.

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 and maintain batch and streaming data pipelines and data products using Python, Airflow, dbt, Pub/Sub, and BigQuery. Develop data quality, observability, monitoring, and alerting frameworks; manage Terraform-based GCP infrastructure, access controls, cost optimization, and GDPR compliance. Collaborate with engineers, data scientists, analysts, product managers, and operations teams while developing AI tooling to improve data engineering productivity and self-service platform usage.

Build and operate production data pipelines for patent, research, and company data using Python and Dagster. Model messy real-world data, develop agent evaluation harnesses, implement data quality monitoring, and deliver reliable data to product stores and APIs. Collaborate with product and ML teams, evaluate new data sources, troubleshoot source failures, and define measurable standards for data quality. The role involves working with large, noisy datasets and shaping how AI agents support data processing.

Build, deploy, and maintain scalable data platforms and ETL/ELT pipelines for structured and unstructured data. Develop data applications, conduct analytical exploration, and guide multidisciplinary teams delivering mission-focused solutions. Responsibilities include retrieving, parsing, processing, and organizing disparate data for reporting, analytics, fraud detection, cancer research, and national intelligence in an agile environment.

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

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role is onsite in a SCIF in Stuttgart and requires an active Secret clearance, U.S. citizenship, and potential eligibility for higher clearances.

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, deploy, operate, and maintain scalable data and AI solutions for mission users. Responsibilities span requirements discovery, full-stack and data engineering, ETL/ELT pipelines, data science, modeling and simulation, agentic AI workflows, testing, deployment, training, and sustainment. The role partners directly with senior mission stakeholders, integrates emerging technologies, and supports multiple operational projects in classified environments.

Build and operate OrderYOYO’s production data platform and Microsoft Fabric lakehouse. Responsibilities include developing Python and PySpark pipelines, designing incremental ingestion and semantic models, managing performance and capacity, leading legacy-platform migrations, monitoring data quality, resolving production issues, and embedding AI, RAG, and applied machine learning into data products. The role partners with Finance, Product, executives, analytics, and engineering teams.

Build and operate production-grade data pipelines and infrastructure supporting enterprise AI agents and applications. Establish data governance, privacy controls, lineage, auditability, quality validation, drift detection, and knowledge graph capabilities. Manage data products across their lifecycle, define engineering standards, mentor engineers, and collaborate with application, platform, data science, and delivery teams globally.

Develop and operate a strategic Google Cloud data platform supporting operational and analytical use cases. Responsibilities include coding, integrating data sources, building batch and near-real-time ETL/ELT pipelines, data modeling and transformation, platform infrastructure and DevOps, and maintaining GCP services. The role also contributes to technical direction, code reviews, mentoring junior staff, and responsible use of AI tools.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

Own the end-to-end development and production deployment of machine learning models for homeowner-partner matching. Responsibilities include defining outcomes, building datasets and pipelines, training and monitoring models, conducting marketplace experiments, improving match efficiency, and shaping the ML stack. The role requires translating model trade-offs into business decisions and collaborating with product, engineering, data, partner management, and leadership stakeholders.

Build interactive data visualizations, maps, dashboards, and 3D experiences that turn complex energy data into clear narratives. Work independently with marketing, policy, press, leadership, and data science teams using React, Next.js, Mapbox, WebGL, and Blender. Own projects end to end, select appropriate visualization techniques, communicate insights effectively, and rapidly adapt to varied project requirements.

Lead aerodynamic modeling strategy for high-agility fighter aircraft. Analyze aerodynamic characteristics, oversee wind tunnel and flight test campaigns, integrate simulation and test data, and define aerodynamic dataset architectures for stability, control, performance, and loads. Guide flight-test data analysis, develop machine learning and data-fusion capabilities, mentor engineers, and provide technical leadership for complex aerospace programs.

Build and operate scalable cloud data pipelines for quantum chemistry datasets used to train AI models. Responsibilities include transforming quantum chemistry software into cloud workflows, managing and validating scientific data, deploying containerized infrastructure through infrastructure as code and CI/CD, and ensuring reliability, security, observability, incident response, and capacity planning. The role collaborates with scientific and technical experts across disciplines.

Build and maintain scalable data applications, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop software for data retrieval, parsing, transformation, reporting, and analytics at enterprise scale. Support planning and operational activities for U.S. Army Europe Africa Headquarters and collaborate on mission-focused technology solutions. Candidates must hold a Secret clearance and bachelor's degree.