Top Data Engineer Jobs in Cologne

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

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.

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.

Owns the end-to-end data processing and dataset lifecycle for robotics AI/ML systems. Designs scalable pipelines that convert raw sensor recordings into reliable training, validation, benchmarking, and regression datasets. Implements data quality checks, versioning, lineage, reproducibility, distribution, and infrastructure integration across cloud and compute environments. Develops dashboards monitoring data quality, scenario coverage, labeling, and ML readiness while collaborating with AI/ML, perception, validation, and platform engineering teams.

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.

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.

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 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.

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.

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.

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 and build large-scale distributed data systems for blockchain analytics. Translate ambiguous product requirements into technical designs, sequence work, make architectural decisions, and own components from design through production. Work across Go, Kotlin, Rust, and Python while optimizing data ingestion, distributed processing, and data lake storage. Mentor engineers, improve design reviews and testing practices, and collaborate effectively in a fully distributed team.

Design high-speed data connector and PCB solutions using SolidWorks, CATIA, Altium Designer, and Mentor Graphics Xpedition. Apply DFM/DFT principles, signal integrity practices, and ANSYS FEA/CFD simulations to evaluate manufacturability, structural integrity, thermal behavior, and electrical performance. Manage cost, mass, timing, design changes, documentation, revisions, and product data through Siemens Teamcenter PLM.

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.

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.

Build scalable data pipelines and architectures for data and AI products, lead cloud data migration to Azure, optimize large-scale datasets, and support reporting and analytics. Collaborate with analysts, ML engineers, DevOps, backend, product, and business teams. Promote clean code, automation, maintainability, and continuous improvement across the data platform.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.