Data Quality Experts in Cologne
matched in minutes from over 15,000 CVs with the power of AIHire experts who clean messy datasets, define validation rules, and set up monitoring for broken pipelines and bad source data. They also improve data quality management across BI, analytics, and operational systems, with fast, precise matching from vetted, available freelancers.
Meet FRATCH Experts in Cologne, who have recently used Data Quality
Hooman Behmanesh
Last position:
Fullstack Developer at Möbel Roller GmbH
- Further development of the existing e-commerce platform based on SAP Commerce (Hybris) to meet the growing demands of digital commerce.
- Ensuring the scalability and performance of the backend, so the platform remained stable and efficient even under heavy user load.
- Development and integration of new OCC REST APIs and services for modular extensions and flexible adjustments, to implement new features quickly.
- Optimization of data flows and interfaces, which significantly improved platform efficiency and system performance.
- Ensuring a maintainable and scalable code base by using Clean Code principles, proven design patterns, and a future-proof architecture.
- Reduction of errors through extensive testing with JUnit, Mockito, and load tests with Gatling, supported by the introduction of automated test processes.
- Improved system performance through targeted refactoring measures and efficient database queries, especially to handle peak loads.
- Use of modern cloud and monitoring tools such as Kubernetes, Google Cloud Platform (GCP), and Grafana to ensure a stable and monitored infrastructure.
- Clear improvement in efficiency, scalability, and reliability of the platform, which now meets the demands of a dynamic and growing e-commerce market.
Loretta Acheampong
Last position:
Master Thesis Student at Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Future Fuels
- Managed end-to-end data workflows for LCA and carbon footprint assessments, including data collection, validation, structuring, and LCI modeling for metal alloys, ensuring high data quality and traceability.
- Developed a basic Excel automation tool to simplify carbon footprint calculations of metal alloys, replacing repetitive modelling in the software that previously took several hours.
- Communicated environmental impact results through quantitative analysis, visualizations and reports to support the broader project strategy.
- Performed environmental hotspot and scenario analysis to identify key impact drivers and assess opportunities for emissions reduction.
Henning Uiterwyk
Last position:
Senior Expert Data Governance, Master Data Quality and Data Migration at E.ON
- Planning and implementation of a migration strategy for master and transaction data for the continuous loading of a cloud-independent database
- Creation and pilot implementation of a company-wide Business Data Model for customers, suppliers, contracts, products, prices, consumption, invoices, and dunning
- Concept and consulting for a Data Governance Framework incl. definition of committees, roles, processes, and metadata model
- Operationalization of the Data Governance Framework with definition of data standards
- Sub-project management in two pilot projects (PoCs) for Data Governance systems (ErwinDIS and Atlan)
- Training and coaching the data team in migration, data quality, and data modeling
Markus Blohm
Last position:
Consultant, Technical Project Manager at Lanxess
- Project: ESU Windows and SQL Server consolidation / Configuration Management in ServiceNow
- Creation of an as-is analysis of all servers worldwide
- Creation of an as-is analysis of all SQL servers worldwide
- Creation of requirements analyses for SQL and server migrations
- Coordination of requirements with partners for migrations to Hyper-V (on-premises) or Azure Cloud
- Moderation of jour fixe meetings
- Creation of roadmaps and solution models for server migrations
- Setup of test scenarios
- Moderation of workshops for the introduction of a Global Admin Team
- Creation of data models (CMDB) in ServiceNow
- Conducting workshops for the CMDB data model
- Creation of solution models for the CMDB
- Creation of seamless configuration and work documentation for the CMDB
- Creation of reports for license management
- Creation of KPIs for data quality in ServiceNow
- Creation of a service catalog
- Moderation of workshops for creating service requests
- Interface between needs analysis and ServiceNow development team
- Systems used: Windows 7, Windows 10, Microsoft Office 365/2010, Windows Server 20xx, SQL Server 20xx, SharePoint, Azure Cloud, Hyper-V, ServiceNow, various tools
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
Nenad Biresev
Last position:
Safety Video Analytics Project for Airbus at Airbus
- Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
- Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
- Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.
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.
Dr Stefan Neuwirth
Last position:
Independent Consultant
- Intended break
- Lecturing at universities
- Mentoring
- Social engagement
- Coaching education
- Founding of research institute “Center For Impactful Organization Design (CIOD)”
Johannes Wagner
Last position:
Senior Data Engineer at Soorce GmbH
- Analysis of business requirements
- Integration of different data sources such as ERP systems, production systems, and external data sources
- Implementation of load processes and processing logic with MSSQL
- Data modeling and optimization of data models
- Setting up data quality management incl. data profiling with dynamic programming
- Support in designing and establishing data governance, especially in the areas of data quality management and data protection
- Support in developing BI solutions with Tableau to help decision-making processes
Stanislav Stolberg
Last position:
Interim CTO / IT Consultant (Cloud & App Security · AI & Web3) at Deutsche Bank Group; Startups
- Spearheaded strategic and operational oversight of IT infrastructures to accelerate innovation and ensure audit-proof delivery.
- Acted as key liaison between management, business departments, and engineering, actively engaging in coding, cloud architecture, and CI/CD to resolve critical path challenges.
- Engineered and implemented an AI Governance Program to manage risks and ensure compliance with the EU AI Act, reducing AI use-case approval times from 8 to 3 weeks.
- Delivered and deployed secure AI systems into production (RAG-based knowledge platforms), resulting in a 35% decrease in standard support ticket volume.
- Established robust security standards and governance frameworks for APIs (OAuth2/OIDC, mTLS) and cloud platforms (AWS/GCP) to guarantee compliance and system integrity.
- Hardened cloud infrastructure by implementing Zero Trust principles and a comprehensive observability stack (logging/alerting), achieving 99.9% availability in a 24/7 on-call environment.
Alexander Vasiliev
Last position:
Senior DevOps / Platform Engineer at Kaufland e-commerce / real.digital (ex hitmeister.de)
- Evolved the platform from bare-metal infrastructure with a monolithic PHP application to a hybrid Google Cloud architecture with Go microservices on Kubernetes
- Managed and scaled a production infrastructure with 300+ VMs and dozens of clusters, including MySQL, PostgreSQL, MongoDB, RabbitMQ, Redis and Elasticsearch clusters
- Designed architecture, deployment, monitoring, performance analysis, and upgrades for all platform services using Terraform and Ansible
- Migrated observability systems from ELK and Prometheus to Datadog, managed with Terraform
- Introduced Jenkins CI/CD with SonarQube integration and automated tests to speed up feedback cycles
- Containerized the testing environment and implemented GitLab CI pipelines for Docker image builds, static code analysis, and infrastructure tests
- Performed zero-downtime migrations of MySQL and MongoDB clusters to Google Cloud
- Developed reusable Ansible roles for database clusters and automated data obfuscation for staging environments
- Decomposed monolithic databases and migrated to microservice architectures
- Built tools to detect and optimize slow queries in MySQL and MongoDB
- Conducted online schema migrations with pt-online-schema-change without downtime windows
- Developed internal Go applications for batch queries, GitLab-Jira integration, and MongoDB backup recovery
- Technologies: Debian, Ubuntu, Alpine, Go, Bash, Python, PHP, SQL, GitLab CI, Jenkins, Drone CI, MySQL, MongoDB, PostgreSQL, Redis, Elasticsearch, RabbitMQ, Kafka, Docker, Kubernetes, Nomad, Ansible, Terraform, Grafana, ELK, Prometheus, Datadog, Nagios, Zabbix, Nginx, HAProxy, Vault, Helm, SonarQube, Filebeat, Auditbeat, Google Cloud, AWS
Michael Tomaschewski
Last position:
B2B Growth Consultant at Loy & Co. Corporate Finance GmbH
Optimization of inbound and outbound processes for lead generation for B2B sales with a focus on prospect data for the software and IT target group
Setting up a structured cold e-mail marketing program for B2B lead generation in the German mid-sized market
Derivation of various optimization measures for inbound growth processes (website, SEO)
Generation of a double-digit number of leads from mid-sized companies through cold e-mail campaigns
Optimization of the CRM system Pipedrive for structured growth campaigns
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
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
Discover over 15,000 top freelancers
Statistics of experts using Data Quality
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 16 years)
Position duration
2 years (Germany: 3 years)
Positions per freelancer
11 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Quality Assurance
Top industries
Information Technology, Professional Services, Manufacturing
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
94% (Germany: 96%)
Master's degree or higher
76% (Germany: 68%)
Doctorate
12% (Germany: 11%)
Certifications per freelancer
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 Data Quality
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
Data quality work
Data Quality is the practice of keeping data fit for use. It covers accuracy, completeness, consistency, timeliness, and uniqueness across databases, files, and pipelines. Strong specialists turn unreliable data into trusted input for reporting, operations, and automation.
What specialists do
- profile source tables and define business rules
- build checks for missing, duplicate, and invalid values
- review lineage, exceptions, and root causes
- design fixes for upstream systems and recurring defects
- document ownership for critical datasets
Common tool stack
Data Quality projects often use SQL, Python, dbt, Great Expectations, Soda, Informatica, Talend, and cloud-native checks in Snowflake, BigQuery, or Databricks. Experienced professionals know how to combine rule-based validation with pipeline monitoring and clear alerting.
When companies hire
Teams usually bring in freelance experts when reports conflict, migrations expose old defects, or new interfaces start feeding bad records into dashboards and workflows. In Cologne, this often matters for logistics, retail, media, and industrial groups that depend on stable reporting across local and global systems.
What good experts deliver
A strong specialist does not only spot broken rows. They trace problems to their source, write tests that stay maintainable, and agree on pass or fail rules with business owners. They also make sure data quality checks fit the release process, not just ad hoc cleanup.
Signs you need help
- dashboard figures do not match source systems
- duplicate customer, product, or order records keep appearing
- new feeds fail after schema changes
- teams argue about which data is correct
- there is no repeatable process for DQ issues
Frequently asked questions
Need clarity? These are the questions we hear most often about Data Quality.
Data Quality covers the controls that keep data accurate, complete, consistent, and usable. In practice, that means validation rules, exception handling, root-cause analysis, and cleanup across source systems, warehouses, and reporting layers. It is as much about preventing bad data as it is about detecting it.
No. DQ is broader than data cleansing because it includes profiling, monitoring, rule design, ownership, and ongoing prevention. Cleansing removes or fixes bad records, while Data Quality management tries to stop the same issues from coming back.
A company usually brings in a Data Quality specialist when reporting is disputed, migrations reveal hidden defects, or new integrations start producing unreliable records. Freelancers are also useful when the team needs help defining business rules for critical datasets without slowing delivery.
A strong Data Quality expert usually works with SQL and often Python, plus tools like dbt, Great Expectations, Soda, Informatica, Talend, or cloud data stacks. Useful adjacent skills include data modeling, ETL or ELT design, metadata management, and clear communication with business owners.
Data Quality focuses on whether the data itself meets defined rules and business expectations. Data observability is broader on operational signals such as freshness, volume, and pipeline health. In many teams, the two work together: observability spots issues early, while Data Quality defines what broken data actually means.
It depends on the scope, but Data Quality work is rarely a pure entry-level task. For a focused cleanup or rule set, a specialist with hands-on project experience may be enough; for enterprise governance or complex pipelines, you want someone who has handled source-to-report tracing, stakeholder alignment, and durable test design.
Yes. Data Quality work is often remote-friendly because most tasks happen in SQL, notebooks, and data platforms. On-site time can still help when rules depend on local business processes, sensitive source systems, or workshops with teams in Cologne and nearby offices.
Look for someone who can explain the business meaning of a rule, not just write checks. A strong Data Quality professional can show how they found the root cause, how they reduced repeat defects, and how they made the process maintainable for the team that follows.
The average hourly rate of freelancers in Cologne, Germany who have used Data Quality in their recent projects is 109 €, which corresponds to a daily rate of about 870 € based on an 8-hour working day.
Of the freelancers in Cologne, Germany who have used Data Quality in their recent projects, 94% hold at least a Bachelor's degree, 76% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers in Cologne, Germany who have used Data Quality in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Cologne, Germany who have used Data Quality in their recent projects are German (100%), English (100%), and French (21%).
The most common industries among freelancers in Cologne, Germany who have used Data Quality in their recent projects are Information Technology (74%), Professional Services (53%), and Manufacturing (47%).
The most common business areas among freelancers in Cologne, Germany who have used Data Quality in their recent projects are Information Technology (89%), Business Intelligence (79%), and Quality Assurance (68%).
Main locations of FRATCH Experts, who have recently used Data Quality
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 
Stuttgart 
Dusseldorf 
Hanover 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
