
Big Data Experts in Cologne
for reliable data products, matched in minutes with AIHire experts who design data platforms, build streaming pipelines and turn complex datasets into usable insights. FRATCH precisely matches you with vetted, available freelancers who fit your Big Data project and can start quickly.
Meet FRATCH Experts in Cologne, who have recently used Big Data
Alexander B.
Last position:
Senior Data Engineer at RWE AG
Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.
Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows
Beshr A.
Last position:
System Administrator – HealthCare IT & Data Infrastructure at Cellitinnen Hospital Association
- Integration of medical modalities (including ultrasound) into the existing IT infrastructure (DICOM, HL7) – put into operation within the planned timeframe.
- Administration and optimization of PACS systems for efficient archiving and distribution of radiology image data across multiple locations.
- Ensuring consistent data quality and seamless interoperability in data exchange between HIS, RIS, and PACS.
- Close collaboration with medical staff to analyze and digitally optimize clinical workflows.
- Requirements management and test coordination when implementing clinical requirements in complex IT structures.
Maurice K.
Last position:
Solution Architect – Cloud-Native Transformation of IoT Monitoring Platform at NDA / Wind Energy Sector
- Led the end-to-end architecture and migration of a legacy on- premise IoT monitoring system to a cloud-native Azure platform within a 17-member development team
- Designed and implemented a scalable microservices architecture (Java 21, Spring Boot, Kubernetes, Azure Services), decoupling IoT data streams and eliminating legacy system bottlenecks
- Defined technology stack, mentored developers, and orchestrated cross-functional teams in 4 countries to ensure high-quality delivery and alignment with architectural standards
- Drove requirements engineering and system redesign, removing years of technical debt and introducing event-driven processing and automated workflows
- Key Achievements
- Increased system stability and uptime by ~10x, eliminating need for 24/7 DevOps intervention
- Reduced hosting costs by ~80% (5x savings) through cloud optimization
- Improved performance and scalability, enabling stable handling of high-volume IoT data streams
- Delivered successful zero-disruption migration from on-prem to cloud, with strong user satisfaction and reliability from day one
Halil O.
Last position:
Senior Cloud Operations & DevSecOps Engineer (Azure / Terraform / CI-CD) at KfW Bankengruppe
Regulated environment within a German banking group (approx. 8,500 employees, hybrid cloud strategy).
Responsible for operating, provisioning, and continuously securing business-critical platforms – including a GenAI chat application, a big data/AI platform, and data science workspaces based on Azure Virtual Desktops and VMs. Ownership of Azure DevOps projects for ShaiHulud and React2Shell, as well as BSI alerts – Security Operations improvements across the SDLC.
Deployment responsibility for the GenAI chat application, big data/AI platform (BDAI), and data science workspaces (AVD/VM-based) in the respective landing zones.
Deployment & release management: end-to-end responsibility for deploying portal and service applications across multiple Azure landing zones, including technical approvals, compliance with development team deployment guidelines, and ensuring ITIL-based change and release processes via ServiceNow.
Azure landing zones & network architecture: design, provisioning, and operation of Azure landing zones for 3-tier web applications with enhanced network segmentation, VNet peering, hub-and-spoke architectures, private endpoints, and firewall integration across separate subscriptions and tenants.
Azure DevOps governance & operations: ownership of the Azure DevOps organization, including projects, repositories, and CI/CD pipelines; implementation of governance requirements such as branch policies, approval gates, permission models, and audit-ready operating structures.
Infrastructure as Code (Terraform): design, implementation, and operation of a modular Terraform architecture for standardized cloud infrastructure deployment, including state management, provider versioning, reusability, and policy-as-code approaches.
CI/CD pipeline engineering: design, operation, and optimization of complex YAML-based CI/CD pipelines with multi-stage deployments, template standardization, self-hosted agents, integrated secret management, and automated quality and security checks.
Git migration & platform consolidation: planning and execution of repository and pipeline migration from Azure DevOps to GitLab CI/CD, including automated scripts, full Git history transfer, pipeline porting, and platform consolidation.
Container & platform operations (AKS): operation and security assessment of containerized workloads on Azure Kubernetes Service, centralization of on-premises container registries for ACR.
OpenShift (OCP) security reviews: security assessment of code baselines, build pipelines, and deployment processes for on-premises OpenShift clusters with critical applications, and derivation of specific hardening recommendations.
Shift-left security & DevSecOps transformation: introduction of a company-wide shift-left approach for early security integration in development and deployment processes, enabling developers to perform self-led security checks and sustainably reduce vulnerabilities before production (IDE integrations, pre-commit hooks, local scanners).
Software supply chain security: analysis and mitigation of supply chain risks in NPM- and Yarn-based applications through dependency audits, CI/CD pipeline hardening, token rotation, and restriction of risky build and lifecycle mechanisms.
Frontend & framework security (React / Next.js): security assessment and coordination of critical vulnerability remediation across platform applications and web frameworks, including coordination and complementary technical mitigations with all teams following BSI alerts.
Software composition analysis (SCA): introduction and operation of automated vulnerability scans for container images, pipelines/artifacts, and third-party dependencies, including SBOM exports within CI/CD pipelines.
SAST/DAST integration: design and piloting of static and dynamic application security tests in close collaboration with security architecture and development teams, for continuous improvement of code and runtime security, and establishing operational acceptance tests.
Artifact & registry consolidation: analysis and consolidation of all package and container repositories for service applications and AKS workloads, aiming for a centralized, secured registry strategy with centralized vulnerability scanning and governance.
Dependency-Track & SBOM strategy: advising the compliance board on introducing a central SBOM and vulnerability management platform to increase enterprise-wide dependency transparency and accelerate CVE response capability.
CI/CD pipeline hardening: security analysis and cleanup of the existing pipeline landscape by removing unused pipelines, improving secrets hygiene, implementing least-privilege principles, and isolating build agent environments.
Azure Web Application Firewall (WAF) optimization: analysis and tuning of existing Azure WAF rules (OWASP Top 10 Core Rule Set, DSR/SDC, custom rules) to defend against known vulnerabilities and exploit patterns, including reducing false positives and improving threat detection.
Documentation & stakeholder communication: creating and maintaining technical documentation, runbooks, and architecture overviews in Jira and Confluence, as well as active knowledge transfer between operations, development, security, and compliance stakeholders.
Emmanouil T.
Last position:
Senior Analytics Engineer at Trade Republic Bank GmbH
- Implementation of analytics and automation solutions for the Anti Financial Crime business unit
- Providing the infrastructure, including reusable data models and feature ingestion for production ML and rule based models in the areas of Account Take-Over and Card fraud detection, as well as Customer Risk Assessment
- Tools used: Snowflake, dbt, Looker, AWS, Python, Airflow, Metaflow
Yannick T.
Last position:
Cloud Architect at Anonymous
- Implementation of Infrastructure as Code (IaC) with Terraform to ensure a scalable, repeatable, and secure Azure infrastructure
- Implementation and optimization of CI/CD pipelines with Azure DevOps
- Management of container and server environments and AKS
Kevin B.
Last position:
Procurator and AI Lead at ValueData GmbH
- Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
- Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
- Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
- Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.
Dmitriy D.
Last position:
Freelance Senior Data Scientist at Merck KgaA
- AWS
- Genedata Profiler
- Data Lake
- APIs
- Rstudio
- GitLab
- Python
- R
- Data acquisition, integration, and simulation
- Multiplex immunofluorescence
- Copy-number variation calling
- HLA typing and loss-of-heterozygosity analysis
- RNA expression analysis
Peter B.
Last position:
Data Warehouse Consultant (Development and Analysis) at Atruvia AG
- Developed and enhanced ETL loading jobs with IBM DataStage and optimized SQL in an IBM DB2 environment as part of the Agree21 data migration
- Analyzed data quality and developed test procedures
- Created PowerShell scripts and documented GIT deployment processes
- Technologies: RedHat Linux, IBM DB2 with DBVisualizer, IBM InfoSphere DataStage 11.7, JIRA, TortoiseGIT, TortoiseSVN, PowerShell scripts
Dirk H.
Last position:
Owner at Soorce GmbH
- On secondment to REWE digital GmbH
- Agile team coach for a brick-and-mortar retail team
- Coaching, lateral leadership and facilitation
- Establishing agile values and applying agile methods
- Team development and conflict moderation
- Facilitating company-wide strategy workshops
- Technologies: Kanban, Scrum, Microsoft Teams, Liberating Structures, Jira, Confluence, Concept Board, Miro, Test-Driven Development, OOP, DBMS (SQL, MySQL, MariaDB, PostgreSQL)
Discover over 15,000 top freelancers
Statistics of experts using Big Data
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 19 years)

Position duration
1.5 years (Germany: 3 years)

Positions per freelancer
10 (Germany: 12)

Top business areas
Information Technology, Business Intelligence, Product Development

Top industries
Information Technology, Banking and Finance, Healthcare

Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
78% (Germany: 92%)
Master's degree or higher
67% (Germany: 65%)
Doctorate
22% (Germany: 17%)

Certifications per freelancer
6 (Germany: 3)

Most common languages
German, English, French

Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Cologne are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Cologne using Big Data
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Big Data experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (70%)
- Banking and Finance (50%)
- Healthcare (50%)
- Insurance (50%)
- Education (40%)
- Manufacturing (40%)
- Biotechnology (30%)
- Chemical (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Big Data means
Big Data covers the methods and systems used to collect, store, process and analyse data at a scale or speed that traditional databases cannot handle efficiently. It supports structured, semi-structured and unstructured information from applications, devices, transactions, logs and connected systems. The goal is dependable access to useful insights, not data volume alone.
What it builds
Companies use Big Data to create data lakes, analytical platforms, recommendation services, fraud detection workflows and forecasting models. It also supports customer analytics, supply chain visibility, industrial monitoring and near-real-time reporting. Strong foundations make data available to analysts, applications and machine learning systems without losing control of quality or access.
- Batch and streaming data pipelines
- Data lakes and warehouse integrations
- Event-driven analytics and monitoring
- Feature stores and machine learning data flows
Ecosystem and tooling
The ecosystem commonly includes Apache Hadoop, Apache Spark, Apache Kafka, Apache Flink and cloud services from AWS, Microsoft Azure and Google Cloud. Professionals may also work with object storage, columnar formats such as Parquet, SQL engines, orchestration tools and observability systems. The right combination depends on latency, governance, data formats and operating constraints.
When expertise matters
Freelance specialists are useful when a company is modernising a legacy warehouse, consolidating fragmented sources or moving from batch jobs to streaming. They can establish ingestion patterns, improve pipeline reliability, set retention rules and connect analytical workloads to operational systems. In Cologne, collaboration may combine remote delivery with on-site workshops for teams in manufacturing, logistics, media or retail.
What strong professionals deliver
Strong Big Data professionals understand both distributed processing and the business questions behind it. They define clear data contracts, handle schema changes, protect sensitive information and make failures visible. They also document decisions, test pipelines and control cloud consumption instead of treating infrastructure as an afterthought.
- Traceable data lineage and ownership
- Recoverable pipelines with meaningful alerts
- Secure access and sound governance
- Documentation that internal teams can maintain
Choosing the right specialist
Start with the data sources, expected workloads and decision the system must support. Ask for examples of comparable ingestion, processing or migration work, then examine how the professional measured quality and handled failure cases. Relevant experience with Kafka, Spark, Hadoop, cloud storage, SQL and orchestration is valuable, but communication and practical trade-off decisions matter just as much.
Frequently asked questions
Everything clients usually want to know about Big Data, in one place.
Big Data is used to process large, fast-moving or varied datasets that conventional systems struggle to handle. Common applications include customer analytics, fraud detection, recommendation engines, industrial monitoring, supply chain analysis and machine learning preparation.
Big Data platforms are designed for varied data types, distributed processing and high-throughput workloads, while a traditional warehouse usually focuses on structured, curated reporting data. The two often work together, with a lake or distributed store supporting ingestion and a warehouse serving governed business analysis.
Big Data work often connects with cloud infrastructure, SQL, data modelling, DevOps, information security and machine learning. Experience with Apache Kafka, Apache Spark, Apache Flink, Hadoop, orchestration and observability can also be important, depending on the project.
Big Data projects need practical experience that matches their complexity, data sensitivity and operational demands. For a pipeline change, focused delivery experience may be enough; a platform migration or streaming system calls for someone who has designed, operated and troubleshot comparable systems.
Big Data work is often suitable for remote collaboration because repositories, cloud environments and monitoring tools are accessible online. Teams in Cologne may still benefit from on-site workshops for architecture decisions, data ownership discussions or coordination with operational departments.
Big Data quality depends on more than a list of tools. Review how the specialist handles schema evolution, data lineage, testing, access control, recovery and monitoring, and ask how past solutions supported a clear business outcome.
Big Data is the broader discipline covering data architecture, processing, storage and analytics. Hadoop is one ecosystem that provides distributed storage and processing components, while modern solutions may also use Spark, Kafka, Flink, cloud-native services or warehouse technologies.
Big Data pipelines are reliable when they have clear contracts, validated inputs, repeatable processing and visible failure handling. Good specialists add monitoring, lineage, recovery procedures and documentation so teams can identify stale or incomplete data before it affects decisions.
The average hourly rate of freelancers in Cologne, Germany who have used Big Data in their recent projects is 118 €, which corresponds to a daily rate of about 941 € based on an 8-hour working day.
Of the freelancers in Cologne, Germany who have used Big Data in their recent projects, 78% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Cologne, Germany who have used Big Data in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Cologne, Germany who have used Big Data in their recent projects are German (100%), English (100%), and French (40%).
The most common industries among freelancers in Cologne, Germany who have used Big Data in their recent projects are Information Technology (70%), Banking and Finance (50%), and Healthcare (50%).
The most common business areas among freelancers in Cologne, Germany who have used Big Data in their recent projects are Information Technology (100%), Business Intelligence (80%), and Product Development (70%).
Main locations of FRATCH Experts, who have recently used Big Data
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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