Big Data Experts in Cologne
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Meet FRATCH Experts in Cologne, who have recently used Big Data
Halil Oeztoprak
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.
Alexander Bromberg
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 Alnirabieh
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 Knopp
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
Emmanouil Tzouridis
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 Tessa
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 Baßler
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 Drichel
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 Brungs
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 Hesse
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: 91%)
Master's degree or higher
67% (Germany: 64%)
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Big Data is the work of storing, processing, and analyzing large data sets that ordinary tools cannot handle well. It shows up in analytics, event streams, log data, customer behavior, and machine data. The goal is simple: make data usable for decisions, automation, and reporting.
Common stacks
- Hadoop for distributed storage and batch processing
- Spark for fast transformation and analytics
- Kafka for event streaming and data movement
- Hive, Trino, or similar layers for querying data at scale
- Cloud warehouses and lakehouse setups for modern data teams
Strong specialists know where each tool fits and where it does not.
Typical projects
Companies bring in Big Data specialists for new pipelines, migration work, performance tuning, and data platform recovery. They also help when a reporting setup is too slow, data quality is unclear, or teams need one view across many sources.
In Cologne, this often matters for media, logistics, retail, insurance, and industrial data environments that combine on-site systems with cloud services.
What good specialists do
- Design clear ingestion, storage, and processing flows
- Write efficient Spark jobs and stable batch or stream logic
- Improve data quality checks and failure handling
- Document schemas, lineage, and handover steps
- Work closely with analytics, product, and operations teams
Skills around the work
Big Data work usually sits next to SQL, Python, cloud services, Linux, and data modeling. Many projects also need experience with orchestration, monitoring, security, and cost control. The best professionals make the whole flow easier to operate, not just faster to build.
When to bring in help
Use outside expertise when internal teams are short on platform knowledge, need a second opinion on architecture, or have a deadline tied to a migration or launch. Freelance support also helps when you need focused help for a narrow problem instead of a long hiring process.
Remote work fits well for design, coding, review, and troubleshooting. On-site time in Cologne can help when access to internal systems, stakeholders, or data governance discussions is important.
Frequently asked questions
Everything clients usually want to know about Big Data, in one place.
Big Data is used to collect, store, and analyze large volumes of operational, customer, and machine data. Teams use it for dashboards, forecasting, fraud checks, event analysis, and data-driven product decisions. It is especially useful when data arrives from many systems and needs to be processed in a controlled way.
Big Data is the broader work of handling data at scale, while Hadoop and Spark are parts of the toolset. Hadoop is often linked to distributed storage and batch jobs, and Spark is widely used for faster processing and analytics. A good specialist knows how to combine these tools with storage, orchestration, and querying layers.
A company usually needs a Big Data specialist when pipelines are slow, data sources are growing, or reporting is unreliable. The need also appears during cloud migration, platform redesign, or when teams must unify batch and streaming data. If the system is becoming hard to operate, outside help is often worth it.
A strong Big Data freelancer usually brings SQL, Python, cloud knowledge, and data modeling skills. Experience with Kafka, Spark, Hive, orchestration, monitoring, and Linux is often important too. The best people can also talk clearly with analysts, product teams, and operations staff.
It depends on the scope, but Big Data work often needs someone who has solved real production issues before. Small tasks like a single pipeline fix may need limited support, while platform design or migration work calls for deeper experience. Look for clear examples of performance tuning, data quality handling, and stable delivery.
Yes, most Big Data work can be done remotely because much of it involves code, data models, and system review. For Cologne-based companies, on-site time can still help during access reviews, stakeholder workshops, or sensitive infrastructure discussions. A mixed setup is common when both technical and business input matter.
A good Big Data professional explains trade-offs clearly and does not hide behind tool names. Look for clean pipeline design, attention to failure handling, data quality checks, and documentation that others can use. Strong work should be easy to operate, not just impressive in a demo.
Yes, because Big Data still covers ingestion, streaming, transformation, and platform reliability around those warehouses. Cloud tools handle part of the problem, but they do not remove the need for sound data architecture. Many teams still need help with Kafka, Spark, orchestration, governance, and scale-related troubleshooting.
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 940 € 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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