Top Data Engineer Jobs in Germany

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.

Build and maintain reliable data models and ETL/ELT pipelines across AWS-based data lake layers. Ingest high-volume billing, CRM, network, and customer data; process Kafka events; develop dbt transformations; orchestrate workflows with Airflow; and implement data quality, monitoring, documentation, and reconciliation practices. Collaborate with stakeholders and engineers to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Designs, builds, operationalizes, and maintains enterprise-scale data applications and scalable ETL/ELT workflows. Develops systems for collecting, ingesting, storing, processing, modeling, warehousing, and retrieving structured and unstructured data. Integrates Generative AI, LLM-based workflows, and agentic automation into secure mission systems supporting Intelligence Community objectives. Collaborates with data scientists, analysts, software developers, and stakeholders in Agile environments.

Build and operate a scalable AWS-based data platform and lakehouse. Develop and maintain data pipelines, ingest internal and third-party data, prepare/clean data, create data models for reporting, analytics and AI, and implement testing and monitoring. Collaborate with product and engineering teams and share best practices.

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.

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.

Build scalable backend functionality, algorithms, and data pipelines for industrial IoT products. Responsibilities include optimizing microservice architecture, collaborating with frontend and backend engineers, participating in architecture through testing and maintenance, and evaluating emerging technologies. The role requires modern API development, serverless and streaming architectures, SQL and NoSQL databases, Linux, and backend DevOps technologies.

As a Data Analyst at Air Apps, you'll analyze user behavior and product performance, create reports and dashboards, and provide actionable insights using data visualization tools and programming languages.

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.

Designs, builds, and improves data workflows, backend services, AI agents, pipelines, and workflow orchestration systems. The role centralizes fragmented tools, supports production-grade internal platforms, improves reliability and automation, and translates analyst and stakeholder requirements into scalable technical solutions. Requires strong Python, SQL, data modeling, AI application, Git, and data engineering skills, with collaboration across analysts, engineers, and business stakeholders.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Develop, operate, and optimize a multi-tenant Azure data platform using Data Factory, ADLS Gen2, Databricks, and Synapse Analytics. Build scalable PySpark and Spark ETL/ELT pipelines, maintain data infrastructure, implement CI/CD automation, optimize cost and performance, and support data governance, security, modeling, and reporting. Collaborate with BI and data teams across development, test, and production environments.

Develop and scale web-scale data pipelines that transform raw text into high-quality machine learning datasets. Responsibilities include deduplication, model-based scoring, filtering, toxicity and PII removal, metadata extraction, dataset versioning, provenance tracking, compliance, leakage detection, quality monitoring, and researcher tooling. The role also involves acquiring and evaluating text corpora, collaborating with research, engineering, legal, and governance teams, and developing novel data transformations for AI training.

Support engineering team on machine learning projects: data processing, maintenance, reporting, troubleshooting. Analyze and optimize processes, suggest improvements, and learn industrial practices in a startup environment.

Design, build, and maintain data pipelines and ETL processes; ensure data quality and availability; collaborate with cross-functional teams to support analytics and reporting; implement data solutions and support data platform operations.

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.

Designs and maintains high-throughput Reverse ETL pipelines connecting core platforms with reporting tools. Evaluates storage engines and data models across MySQL, OpenSearch, BigQuery, and ClickHouse; modernizes legacy processing architecture; and partners with engineering teams on scalable information architecture. The role also drives monitoring, reliability, maintainability, technical standards, and secure migration strategies for data infrastructure.

Own and scale the data platform supporting multi-tenant industrial optimization applications. Responsibilities include ingesting heterogeneous customer data, designing storage and query paths for millions of records, managing PostgreSQL performance and migrations, building infrastructure and deployment systems, improving optimization-engine performance, and shipping and operating features in production. The role works across Python, TypeScript, cloud infrastructure, APIs, data pipelines, and the broader product stack.

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.

Design, operate, and optimize scalable ELT pipelines and platform infrastructure for international healthcare data. Build entity-resolution and MDM workflows, implement schema governance and data contracts, optimize cloud storage and warehouse costs, and automate CI/CD for data pipelines while partnering with analytics and domain experts to deliver production-ready datasets.

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.