Top Data Engineer Jobs in Cologne

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.

Designs and operates scalable data integration, transformation, quality, and management solutions for digital R&D. Responsibilities include building data pipelines and models, maintaining a data mesh catalog, implementing CI/CD and infrastructure automation, and supporting monitoring, observability, metadata, lineage, access control, and governance. The role partners with cloud architects and platform teams to digitalize chemical research and development.

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.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

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.

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 and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

Lead technical presales for Microsoft Purview and data security solutions. Advise customers on translating EU and German privacy regulations into technical controls, policies, and architectures. Deliver presentations, demonstrations, workshops, architecture sessions, and proofs of concept. Develop security strategies, overcome technical blockers, support partner engagements, and drive technical decisions through sales and deployment handoff.

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.

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.

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.

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 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 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.