Data Quality Experts in Frankfurt
with precise AI matching and vetted, available freelancersHire experts who improve data accuracy, completeness and consistency across CRM, ERP and analytics environments. Work with specialists in data profiling, validation rules, cleansing workflows and governance, matched quickly to your needs through precise AI matching.
Meet FRATCH Experts in Frankfurt, who have recently used Data Quality
Vadim R.
Last position:
Independent AI Product Lab – Agentic Product Owner / Product Builder | R&D
- Hands-on development of AI-native product prototypes with specialized AI agents for research, requirements, business logic, UX/flow design, test case generation and quality assurance.
- Structured use and orchestration of AI agents through clearly defined roles, inputs/outputs and handover points; breaking down complex product tasks into verifiable work packages and iterative prototyping cycles.
- Establishment of human-in-the-loop quality gates to validate AI-generated results for functional correctness, consistency, completeness and feasibility; targeted rework cycles in case of deviations.
- Development of a regulatory GenAI/rules prototype for CRD VI with a structured decision flow, web UI, rule-based validation and automated test cases; iteration of the business logic through to a pilot-ready POC.
- Design of an AI-to-Action banking prototype: AI intent → consent → bank/product logic → conversion including admin console; translating the product idea into MVP scope, role model, user flows and clickable prototypes.
Justina K.
Last position:
Freelance Consultant for Change & Data Transformation at Freelance Fast Data Consulting
Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present
- Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
- Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.
Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025
- Concept development and solution design for new end-to-end processes including technical interfaces
- Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
- Analysis and validation of data models
- Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
- Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
- Documentation and comments on technical and business requirements to support implementation in agile squads
Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present
- Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
- Development of business solution concepts for migration to the target system, including system integration and data flows
- Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
- Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
- Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.
Jörn M.
Last position:
Freelance Senior Business Analyst / Project Lead / Digital Strategy Consultant at Freiberuflich
- Freelance consulting for startups, fintechs, SMEs, banks, financial service providers, and established companies on strategic, organizational, and technological topics; focus on analysis, structuring, execution readiness, and stakeholder communication.
- Gathering, structuring, and prioritizing business requirements; translating business goals into actionable IT, process, data, and documentation requirements.
- Designing and improving digital platforms, user journeys, portal structures, information architecture, and data-driven product/content structures; strong fit for customer portals, partner portals, and B2B2C application journeys.
- Analyzing user, reach, search, and platform data to derive product, content, and platform decisions; structuring decision bases for management and delivery teams.
- Using Jira, Confluence, Excel, analytics, and automation tools; working in agile and hybrid delivery environments.
Noel L.
Last position:
Founder & Lead Engineer at ausbildung-in-der-it.de
- Platform established and running stably; deliberately reducing my involvement to refocus on an engineering mandate in the financial sector.
- Built an own SaaS learning platform from the ground up and scaled it to over 20,000 users (over 6,000 courses sold, B2C and B2B); end-to-end ownership from development through infrastructure to operations.
- Built a lab environment that provisions an isolated Linux container per user (Docker, Traefik, Go), including automatic provisioning and a dedicated subdomain per user.
- Integrated LLM features into the product and accelerated development end-to-end with AI-assisted workflows (Claude Code, Codex); CI/CD with automated tests.
Fabio N.
Last position:
SAP Logistics & Financials Coordinator at AirPlus International
- Leading technical SAP projects in FI/CO and logistics.
- Advanced ABAP analysis, debugging, and validation of complex integrations.
- Optimization of financial and logistics processes in S/4HANA environments.
- Responsibility for demand management, workshops, and technical solution architecture.
Monika T.
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Christine M.
Last position:
Management consultant at Freelance
Delivery of ICT / DORA management training under DORA Article 5(4) at various banking institutions
Teaching the key content of DORA requirements with a focus on ICT risks, third-party risk management, incident and problem management, and the information register
Deriving implementation measures and recommendations for management and business units
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Christian H.
Last position:
Senior Project Manager at Geodis
- Led a transformation program in corporate finance & procurement across 25+ countries
- Managed end-to-end implementation of SAP S/4HANA and Coupa for 60 regional entities
- Handled post-acquisition integrations including financial reporting, HR systems, and governance
- Directed cross-functional teams, change management, and stakeholder engagement
- Coordinated post-merger integration with the works council for IT system rollouts and GDPR compliance
Eric B.
Last position:
Quality Assurance Lead (QSV) at Federal Employment Agency
Supported the International Web Presence project of the Federal Employment Agency (IntWeb) in quality management, taking on responsibility for the quality of processes and project deliverables while adhering to BA standards. The project's main goals are to give professionals abroad a quick overview of their chances to move to Germany and to enable them to take the necessary steps in a consistently digital way.
Set the fundamental guidelines using the QA handbook
Summarized test results in QA reports for PLA
Analyzed project outcomes for improvement opportunities
Quality management of requirements analysis (especially processes, methods and tools)
Ensured compliance with SERA guidelines
Created a cross-project test concept
Agreed on sprint completion reports
Conducted formal reviews of deliverables according to guidelines and/or project plan
Acted as contact person for internal audit and external audits by auditors or the Federal Audit Office (BRH)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
Polina S.
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Kurt R.
Last position:
Lead Solution Architect (AI HealthTech) / interim CTO & Product Co-Owner at Physio-Agil Frankfurt
- General CTO responsibilities (architectural design, operational setup, external runtime product evaluation, investor buy-in, regulatory compliance).
- Software development oversight (implementation on deep-dive-in) plus workflow design.
- Product co-ownership.
- Tech/tools/frameworks: proprietary software (Java, JavaScript), Kubernetes, Postgres, MiniIO, Ollama (internal), several xAI API (external), OpenTofu (Terraform), Keycloak, Kafka, Prometheus, ELK Stack, GitHub, GitHub Workflows, Argo CD, ISO 27001, BSI-ISM, EU AI Act.
Judith B.
Last position:
BI & Analytics Consulting
- Analysis of web and campaign performance to derive actionable insights for marketing and growth optimization
- Implementation and maintenance of tag management solutions to ensure reliable and consistent data collection
- Continuous development and optimization of reporting structures with a focus on scalability and data quality
- Conducting regular deep-dive analyses and leading monthly stakeholder sessions to present findings and align on optimization measures
- Designing and managing end-to-end data flows from data collection to visualization
- Tools: GA4, Google Tag Manager, Looker Studio, Airbyte, BigQuery
Emil S.
Last position:
IT Project Manager at Eurowings Aviation GmbH
- The project aimed to reduce the scope of the annual PCI DSS audit and increase security in credit card payments.
- Credit card payments were tokenized and data and business processes adjusted so that no personal data was used in the internal IT infrastructure.
- Tools: MS Office, Jira, Confluence, draw.io, Miro, Scrum.
- Designed the tokenization of credit card payments.
- Clarified necessary changes to the architecture and data model.
- Designed and implemented process changes in the call center.
- Prepared management documentation on project status, decisions required, risks and escalations.
Discover over 15,000 top freelancers
Statistics of experts using Data Quality
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 16 years)
Position duration
2.7 years (Germany: 3 years)
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
59% (Germany: 68%)
Doctorate
23% (Germany: 11%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
96% (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 Frankfurt 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 Frankfurt 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Quality 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 (77%)
- Banking and Finance (58%)
- Professional Services (46%)
- Pharmaceutical (35%)
- Healthcare (31%)
- Telecommunication (31%)
- Government and Administration (27%)
- Insurance (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Quality covers
Data Quality is the discipline of making data fit for its intended use. It examines accuracy, completeness, consistency, timeliness, uniqueness and validity across databases, files, APIs and reporting systems. Strong quality practices give teams reliable information for operations, analytics, compliance and customer service.
Where it is used
Companies apply Data Quality controls wherever records move between systems or support important decisions:
- Deduplicating customer, supplier and product records
- Validating addresses, identifiers, codes and reference data
- Monitoring data pipelines and warehouse loads
- Reconciling CRM, ERP and finance information
- Preparing trusted datasets for analytics and machine learning
Ecosystem and tooling
Professionals work across relational databases, data warehouses, lakehouses and integration platforms. Common tools include SQL, Python, dbt, Great Expectations, Soda, Talend, Informatica, Microsoft Purview and Collibra. The right approach connects profiling and testing with catalogues, lineage, metadata and incident workflows.
When companies need specialists
Freelance expertise is useful when quality problems appear after a migration, merger, platform change or rapid growth. Specialists can establish a baseline, define business rules, trace failures to their source and create controls that internal teams can maintain. In Frankfurt, this may support banks, manufacturers, logistics groups and other organisations with complex data flows.
What strong professionals deliver
A capable Data Quality professional translates business definitions into measurable checks and useful remediation steps. They investigate root causes rather than only correcting visible errors, document assumptions and make results understandable to data owners. Their deliverables may include quality assessments, rule libraries, dashboards, monitoring schedules, exception queues and operating guidance.
Working model and outcomes
Data Quality work often spans several teams, so clear communication matters as much as technical skill. Remote collaboration works well when access, ownership and review routines are defined; on-site workshops can help align stakeholders during discovery. In Frankfurt, German and English communication may both be relevant, depending on the organisation and its systems.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Data Quality.
Data Quality is used to ensure that information is accurate, complete, consistent, valid, timely and unique enough for its purpose. Companies apply it to customer data, financial records, product information, operational systems, reporting and machine learning datasets.
Data Quality focuses on whether data meets defined conditions and can be trusted for use. Data governance sets ownership, policies and decision rights, while data observability monitors the health and movement of data systems. The practices work best together.
A strong Data Quality specialist often combines SQL with data profiling, Python or another scripting language, ETL and API concepts, metadata management and data modelling. Experience with cloud warehouses, BI tools, master data and business process analysis is also valuable.
The need depends on the scope and risk of the work. A focused profiling exercise may need a specialist who can quickly assess schemas and rules, while an enterprise programme requires experience with governance, lineage, stakeholder alignment, monitoring and remediation across several systems.
Yes, much of Data Quality work can be performed remotely through secure access, shared documentation and scheduled reviews. On-site sessions in Frankfurt can still help with process discovery, workshops and agreement on definitions when many business teams are involved.
Common Data Quality tooling includes SQL and Python alongside Great Expectations, Soda, dbt tests, Talend, Informatica, Microsoft Purview and Collibra. Tool choice depends on the existing warehouse, integration stack, governance model and the level of automated monitoring required.
Ask for examples showing how the specialist measured quality, found root causes and improved controls rather than only cleaning records. Good professionals explain trade-offs, connect technical checks to business rules and leave behind documentation, ownership and repeatable monitoring.
A Data Quality engagement may produce a data assessment, profiling results, agreed quality dimensions, validation rules, remediation workflows and monitoring dashboards. It can also define ownership and handover procedures so teams can maintain the controls after the engagement ends.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Quality in their recent projects is 100 €, which corresponds to a daily rate of about 804 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Quality in their recent projects, 100% hold at least a Bachelor's degree, 59% hold at least a Master's degree, and 23% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Quality in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Quality in their recent projects are German (100%), English (92%), and French (19%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Quality in their recent projects are Information Technology (77%), Banking and Finance (58%), and Professional Services (46%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Quality in their recent projects are Information Technology (96%), Business Intelligence (81%), and Project Management (69%).
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 
Cologne 
Stuttgart 
Dusseldorf 
Hanover 
Nuremberg Request a free demo
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FRATCH CEO
