
Big Data Experts in Cologne
from over 15,000 CVs, matched by AIHire experts who design scalable data platforms, build Spark and Kafka pipelines, and turn cloud data into reliable analytics. FRATCH matches you quickly and precisely with vetted, available freelancers for your Big Data project.
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
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
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
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.
Dirk H.
Last position:
Owner at Soorce GmbH
- Working on a temporary assignment at REWE digital GmbH
- Agile team coach for a team in brick-and-mortar retail
- Coaching, lateral leadership, and facilitation
- Establishing agile values and applying agile methods
- Supporting team development and facilitating conflict resolution
- 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)
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
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.
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.
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
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: 93%)
Master's degree or higher
67% (Germany: 65%)
Doctorate
22% (Germany: 16%)

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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Cologne are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 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
Data at scale
Big Data describes methods and systems for collecting, storing and processing data volumes that traditional databases cannot handle efficiently. It supports batch analysis, real-time event processing, machine learning preparation and operational reporting across many sources.
What it builds
- Data lakes and lakehouse architectures
- Streaming pipelines for events, IoT and transactions
- Distributed analytics and reporting platforms
- Recommendation, forecasting and fraud detection workflows
These systems help companies turn raw information into usable products, decisions and automated processes.
Core ecosystem
Professionals commonly work with Apache Spark, Apache Kafka, Hadoop, Trino, Flink and cloud services such as Amazon Web Services, Microsoft Azure or Google Cloud. They also use Python, Java or Scala, SQL, object storage, container tooling and orchestration systems. The right combination depends on latency, data quality, governance and operational demands.
When expertise matters
Companies bring in freelance specialists when a data platform must be modernised, connected to new sources or made reliable under changing workloads. Expertise is also valuable when teams need a clear migration plan, stronger data governance or production-ready pipelines without slowing existing operations. In Cologne, remote collaboration can work well alongside on-site workshops, depending on team structure and delivery needs.
Delivery responsibilities
A strong specialist clarifies source systems, ownership, retention and access rules before choosing tools. They design schemas and pipelines, automate testing, monitor freshness and failures, and document how data moves through the platform. They should also connect technical choices to business use cases rather than treating scale as the only goal.
Choosing the right professional
Look for evidence of complete deliveries, not only familiarity with popular tools. A capable expert can explain trade-offs between batch and streaming, warehouse and lakehouse patterns, managed cloud services and self-hosted components. They understand security, lineage, cost control and data quality, and communicate clearly with analytics, product and infrastructure teams. For Cologne-based work, agree early on language, workshop expectations and the balance between remote and on-site collaboration.
Frequently asked questions
Everything clients usually want to know about Big Data, in one place.
Big Data is used to collect and analyse information from many sources at a scale or speed that conventional systems cannot manage efficiently. Common applications include customer analytics, logistics optimisation, fraud detection, sensor processing, personalisation and machine learning.
Big Data environments handle varied data types, distributed processing and high-volume or fast-moving sources. A traditional data warehouse is usually more structured and focused on governed reporting, while modern lakehouse designs can combine both approaches.
A strong Big Data professional often combines distributed processing with SQL, Python, Java or Scala, cloud storage and orchestration. Experience with Apache Spark, Apache Kafka, data modelling, observability, security and machine learning workflows is also valuable.
The required depth depends on the system’s scale, reliability needs and business impact. A narrowly scoped pipeline may suit a specialist with focused delivery experience, while a platform migration or streaming system calls for someone who has owned architecture, operations and data governance end to end.
Big Data work is often suitable for remote delivery because code, infrastructure and documentation are shared digitally. On-site sessions in Cologne can still help with source-system discovery, stakeholder workshops and decisions about ownership or governance.
A Big Data specialist should recommend streaming when actions depend on events arriving with low delay, such as fraud alerts or operational monitoring. Batch processing is usually simpler and more efficient when information can be collected and analysed on a schedule.
Ask how the professional handles data quality, schema changes, access control, monitoring, failure recovery and unclear requirements. A capable Big Data expert can show relevant delivery examples, explain trade-offs plainly and connect technical design to measurable business use.
The Big Data ecosystem commonly includes Apache Spark, Apache Kafka, Apache Flink, Hadoop, Trino and cloud-native storage and processing services. Tool choice should follow workload, latency, governance and team capability rather than popularity alone.
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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