Data Pipeline Experts in Cologne
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Meet FRATCH Experts in Cologne, who have recently used Data Pipeline
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
Piet Althoff
Last position:
Founder at RubberMetrics.com
- Self-hosted table tennis equipment platform.
- Development of a custom “Racket Builder” that uses a co-evolutionary genetic algorithm to identify, evaluate, and recommend the optimal combinations of racket blades and rubbers based on physics heuristics and player data within a search space of over 4 billion combinations.
- Development of a custom fully automated web crawler to capture equipment specifications, integrating an automated pipeline for image normalization as well as data harmonization via DeepSeek.
- Cloudflare Edge Workers written in Rust to perform low-latency data searches and offload computationally intensive simulations from the main server.
- High performance and accessibility standards across a large Nuxt 4 codebase achieving 95–100/100/100 Lighthouse scores.
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.
Rodion Orlinskiy
Last position:
Founder, CTO & Managing Director at MYNR Product Mining GmbH
- Responsible for the architecture and development of an AI-native SaaS platform for industrial product portfolio management.
- Designed the modern data platform architecture on Azure for scalable analytics and enterprise data integration.
- Built enterprise data ingestion and transformation pipelines across complex industrial system landscapes.
- Developed graph-based representations of product structures and dependencies for analytical reasoning.
- Designed and implemented an agentic AI framework for AI-supported decision workflows.
- Built scalable analytical microservices and integrated reporting through modern BI technologies.
- Coordinated backend, AI, and frontend development across the MYNR platform stack.
Jeanne Yap
Last position:
Process Engineering Intern at Procter & Gamble
- Independently initiated and deployed automated validation workflows using Python, cutting manual processing by 58% and improving efficiency
- Developed a machine learning model for synthetic defect generation, reducing downtime and production costs; deployed locally and via Databricks and Azure AI Factory
- Utilized a small dataset of image data from the production lines and extended this dataset with training on models like cycleGAN and pix2pix
- Built and optimized the Linux-based development environment for training 3D models; maintained reproducibility via GitHub
- Presented technical insights to cross-functional teams (engineers, QA, project managers), ensuring alignment of ML solutions with operational needs
André Filip
Last position:
GenAI Product Owner at OW Media Solutions GmbH
- Designed and led the development of an automated short-video generation system.
- Built a scalable AWS backend using Step Functions, Lambda, S3, ECS Fargate, and DynamoDB.
- Developed video rendering with OpenCV and FFMPEG; ensured maintainable Python code.
- Supervised and mentored a Python developer and trained the client in AI workflows.
- Decreased end-to-end production time from hours to minutes.
- Created a modular, extensible architecture designed to support future AI models.
Pappu Prasad
Last position:
Senior Cloud Consultant (AWS Services and Consulting) at devoteam GmbH
- Developed automated ETL pipelines with AWS Glue and Athena to ensure consistent data quality and governance requirements
- Implemented validation, anonymization, and encryption measures for data in compliance with GDPR
- Optimized cloud costs by introducing FinOps practices and increased transparency for business units
- Monitored performance, performed root cause analyses, and ensured adherence to SLAs
- Supported data and solution architects in building scalable data models for ML and analytics scenarios
Giovanni Spinelli Barrile
Last position:
Technical Product Manager at Logicc GmbH
Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.
Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.
Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.
Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.
Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.
Nico Schäfer
Last position:
Quantitative modeling and model development, statistical data analysis, reporting at DB InfraGO / Brockmann & Büchner Partnergesellschaft
- Technical project management, requirements management, and design to guide the data team in developing a predictive maintenance model for DB InfraGO's maintenance planning.
- Statistical modeling and analysis programming with R Studio for fault analysis in preventive maintenance: multivariate modeling using quasi-Poisson, negative binomial, lasso, offset, splines, RandomForest.
- Implementation of various R Shiny dashboards.
- Sparring partner and requirements management for data engineering, data modeling, and ETL pipeline in Tableau Prep.
Discover over 15,000 top freelancers
Statistics of experts using Data Pipeline
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 13 years)
Position duration
1.7 years (Germany: 2.8 years)
Positions per freelancer
8
Top business areas
Business Intelligence, Information Technology, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Finance
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
88% (Germany: 72%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, Spanish
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 Data Pipeline
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
Data pipelines move data from source systems into the tools people use for analysis, reporting, and operations. They are built for batch jobs, streaming flows, ETL, ELT, and data integration work across warehouses, lakes, and dashboards.
Typical work
- Connect APIs, databases, files, and event streams
- Clean, transform, and validate incoming data
- Load data into warehouses or lakehouse stacks
- Monitor failures, delays, and schema changes
Common stack
A strong specialist knows tools such as Airflow, dbt, Kafka, Spark, Fivetran, and cloud services like AWS, Azure, or GCP. They also understand orchestration, scheduling, partitioning, lineage, and how to keep pipelines testable and maintainable.
When companies hire
Companies bring in freelance expertise when a pipeline is slow, brittle, or hard to extend. They also do it for new analytics platforms, migration projects, and urgent fixes after broken jobs or bad source data.
What strong specialists do
Good professionals design for failure, not just for the happy path. They document dependencies, add checks, keep transformations readable, and hand over systems that teams can run without guesswork. In Cologne, this often matters for commerce, logistics, media, and industrial data teams that need clear collaboration with local or remote specialists.
What to expect
A good engagement starts with source systems, data quality, freshness needs, and the target architecture. The best experts ask about ownership, alerting, access, and who consumes the outputs. That keeps the pipeline practical, stable, and easy to evolve.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Data Pipeline.
A strong Data Pipeline turns raw source data into usable data for reporting, analytics, machine learning, and operational systems. It can move data in batches, in near real time, or both, depending on how the business uses it. The goal is clean, reliable flow from source to target.
Not exactly. Data Pipeline is the broader term; ETL and ELT describe common ways to move and transform data inside that pipeline. Many teams still use ETL when transformations happen before loading and ELT when the warehouse does the heavy lifting.
A Data Pipeline specialist often works with Airflow, dbt, Kafka, Spark, and cloud data services. Useful adjacent skills include SQL, data modeling, orchestration, monitoring, and basic Python or Scala for transformation logic. The best fit depends on whether the pipeline is batch, streaming, or hybrid.
A Data Pipeline project can be simple or highly complex, so the right level depends on the sources, volume, freshness, and reliability needs. Small reporting flows need practical setup and good data checks. Larger systems need specialists who can handle retries, lineage, access control, and incident response.
Bring in a Data Pipeline specialist when internal teams are overloaded, the current flow keeps breaking, or a new data platform needs to go live quickly. Freelancers are also useful for migrations, cloud moves, and cleanup work after years of ad hoc scripts. They can focus on delivery without long onboarding.
Most Data Pipeline work can be done remotely because the core tasks are design, coding, testing, and coordination. On-site time in Cologne can help when access to stakeholders, legacy systems, or sensitive internal data is easier in person. Many teams use a mixed setup.
Look for a Data Pipeline specialist who explains trade-offs clearly and shows how they handle failures, testing, and monitoring. Good signs include clean SQL, readable transformation logic, and a practical approach to source quality and ownership. Ask for examples of systems they kept stable over time.
Data Pipeline work is one part of data engineering, focused on moving and shaping data across systems. A broader data engineering profile may also cover modeling, platform design, governance, and analytics enablement. If the project is mainly about reliable data flow, a pipeline specialist is often the best fit.
The average hourly rate of freelancers in Cologne, Germany who have used Data Pipeline in their recent projects is 91 €, which corresponds to a daily rate of about 726 € based on an 8-hour working day.
Of the freelancers in Cologne, Germany who have used Data Pipeline in their recent projects, 100% hold at least a Bachelor's degree and 88% hold at least a Master's degree.
On average, freelancers in Cologne, Germany who have used Data Pipeline in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Cologne, Germany who have used Data Pipeline in their recent projects are German (100%), English (100%), and Spanish (22%).
The most common industries among freelancers in Cologne, Germany who have used Data Pipeline in their recent projects are Information Technology (100%), Education (67%), and Professional Services (67%).
The most common business areas among freelancers in Cologne, Germany who have used Data Pipeline in their recent projects are Business Intelligence (100%), Information Technology (100%), and Research and Development (78%).
Main locations of FRATCH Experts, who have recently used Data Pipeline
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.
Berlin 
Hamburg 
Munich 
Frankfurt 
Dusseldorf 
Essen 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
