Change Data Capture Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Change Data Capture
Paul W.
Last position:
Enterprise Architect at ePrimo GmbH
Led the end-to-end integration and migration architecture for decommissioning SAP IS-U market communication and transitioning to Salesforce Marketing Cloud (SFMC) with E.ON IT Services — covering regulated EDIFACT market messaging, a three-phase data migration (transfer of market message inventory after cutover), redesign of market communication processes, and full GDPR and German energy-market compliance.
- Designed the three-tier target architecture (SAP IS-U → NScale B2B gateway → SFMC) processing 50,000–100,000 EDIFACT messages daily within sub-5-minute SLAs.
- Architected the three-phase data migration: 36 months of historical data, zero-loss cutover of in-flight market messages, and near-real-time CDC synchronization.
- Delivered secure, BDEW/GDPR-compliant integration (AS2/OFTP2, RFC/IDoc, REST APIs, OAuth 2.0, Azure AD, AWS S3) achieving >99.5% uptime, <0.1% migration discrepancy, and RTO <4 hours.
- Applied BPMN to model and optimize the market communication processes.
Technologies: SAP IS-U, NScale, Salesforce Marketing Cloud, EDIFACT, OAuth 2.0, Azure AD, AWS S3, BPMN, Jira, Confluence, Draw.io
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.
Kiriakos K.
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.
Highlights:
- Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
- Formulating a way forward for API Lifecycle Management at OTTO
- Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals
API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
Frank E.
Last position:
DevOps at Lauck-IT
Operations and extensions of Azure DevOps pipelines
Operations and extensions of AWS services
Citrix (Windows 10, Bitwarden)
AWS: ECR, EKS, CloudFront CDN, Route 53, VPC peering and CNI upgrade, Atlas MongoDB, S3 buckets, static website hosting
Azure: build and deploy with DevOps pipelines
Hardeep B.
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Serhat K.
Last position:
Kafka issues in production at Fiverr client
Fiverr client
- Fixed consumer issues in a production Kafka environment.
- Analyzed consumer lag with Datadog.
- Added synthetic tests to detect issues early.
Stack: Kafka, Datadog, Consumer Groups
Klaus E.
Last position:
Exasolution DBA at Rechenzentrum Innsbruck
- DBA Exasolution Server consulting and migration to version 8
- Analysis of SQL query performance and tuning
- Troubleshooting in batch scripts
- Technologies: Gitlab, EXACloud, EXAOperation, DBeaver, Control M
Felix R.
Last position:
SAP Analytics & Planning Expert at RWS GmbH
- Greenfield implementation of S/4HANA Embedded Analytics as well as reporting and planning processes with SAP Analytics Cloud (SAC).
- Integrated production, sales, and inventory planning, as well as financial planning and management reporting.
- Inventory forecasts implemented through SAC Machine Learning.
- Integration of S/4HANA data and HR data (Factorial) into SAP Datasphere (various tables with real-time replication, related data flows, and analytical models)
Benjamin S.
Last position:
Design and implementation of a new cloud service at Porsche AG
- Design of a new cloud service
- Requirements Engineering
- API Design (REST API, Kafka)
- Consulting on architecture, feasibility and effort estimation
- Implementation of a microservice architecture
- Implementation of a Spring Boot web service
- Development of REST APIs including business and persistence logic
- Kafka consumers and producers
- Various Excel upload/download scenarios
- Change Data Capture
- Cloud provisioning with IaC/Terraform
- Go-live with 30,000 users
- Operation, support and bug fixing for other services
Environment/tools: GitLab, JIRA, Confluence, IntelliJ, Spring Boot, Java, Docker, Terraform, AWS ECS, Postgres, Apache Kafka, SAP Datasphere, JUnit, Debezium, Apache POI, Hibernate, JPA, Testcontainers
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
Asad K.
Last position:
Senior AI Developer at Neuland.ai AG
- Architected and deployed a production-scale GraphRAG system using Neo4j, embeddings, and multi-hop reasoning over 120M+ nodes, improving answer precision by 32%, reducing hallucinations by 41%, and lowering retrieval latency by 38%.
- Designed and implemented an enterprise agent ecosystem using Model Context Protocol (MCP), exposing internal APIs, databases, and services as secure callable tools for autonomous workflows and system integration.
- Designed and deployed a production LLM-based email routing agent using Microsoft Graph API, MCP, and Azure OpenAI, achieving 96% routing accuracy, reducing manual triage workload by 65%, and decreasing response times from 18 hours to under 4 hours.
- Implemented autonomous agent self-correction pipelines using iterative feedback loops (Ralph Wiggum), enabling reliable error detection, automated remediation, and production-safe execution.
- Developed a multimodal semantic search platform using multimodal LLMs and vector embeddings, enabling semantic discovery across 250k+ image and video assets and improving search recall by 48%.
Vladislav L.
Last position:
Freelance Data Engineer / Architect at Deutsche Bank
Designed and implemented a scalable data lake on Google Cloud Platform (GCP) for the Anti-Financial Crime (AFC) division, supporting Anti-Money-Laundering and Counter-Terrorism-Financing initiatives
Built robust data pipelines for ingesting and processing over 30 million daily transactions from retail and corporate banking, applying dimensional modeling to enable advanced analytics and regulatory reporting
Ensured resilience by implementing change data capture, late-data handling, and schema evolution across evolving source systems
Led a small engineering team and collaborated with international stakeholders in Germany, Singapore, Sri Lanka, Hong Kong, Taiwan, and Thailand to ensure compliance, data quality, and performance
Google Cloud Platform (GCP)
Apache Spark (Dataproc & Cloudera Data Platform - Python PySpark)
Apache Airflow (Cloud Composer - Python API)
Google Cloud Storage
BigQuery
GitHub Actions
Terraform
Google Kubernetes Engine (GKE)
Discover over 15,000 top freelancers
Statistics of experts using Change Data Capture
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
1.7 years
Positions per freelancer
13
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
93%
Master's degree or higher
36%
Certifications per freelancer
5
Most common languages
German, English, French
Speak two or more languages
93%
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 Germany 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 Germany using Change Data Capture
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.
Change Data Capture 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 (100%)
- Automotive (57%)
- Banking and Finance (57%)
- Telecommunication (43%)
- Manufacturing (36%)
- Pharmaceutical (36%)
- Professional Services (36%)
- Energy (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What CDC does
Change Data Capture, commonly called CDC, records inserts, updates and deletes as they occur in a source system. Instead of repeatedly copying complete tables, it passes only changed data to downstream systems. This supports fresh analytics, event-driven services, integrations and database replication while reducing unnecessary load on production systems.
Where it is used
Companies use CDC to move operational data into modern data estates and connected applications. Typical projects include:
- Feeding warehouses and lakehouses with near-real-time changes
- Synchronising customer, order and inventory data across systems
- Supporting event-driven services and operational reporting
- Migrating databases with limited interruption
- Capturing audit and integration events from transactional systems
Ecosystem and tooling
Strong specialists work across source databases, message brokers, stream processors and target storage. Common tools include Debezium, Kafka Connect, Apache Kafka, Oracle GoldenGate, AWS DMS and cloud-native streaming services. They understand log-based capture, transaction ordering, schemas, offsets, snapshots and connector configuration.
When expertise matters
CDC projects become difficult when schemas evolve, transactions span several tables or downstream consumers require strict delivery guarantees. Companies often bring in freelance expertise during database migrations, platform modernisation, merger-related integration work or the replacement of batch pipelines. In Germany, specialists may need to coordinate remotely with distributed teams or work on site where source systems and compliance processes require close collaboration.
What professionals deliver
A capable professional starts by mapping source transactions, data owners and consumer requirements. Deliverables can include an architecture, connector configuration, infrastructure definitions, monitoring dashboards, replay procedures and operational documentation. The work should also define how to handle deletes, duplicate events, late delivery, failed connectors and schema changes.
How to assess quality
Look for practical experience with the exact source databases, target platforms and delivery expectations in the project. Ask how the specialist prevents data loss, validates completeness and restores a pipeline after failure. Good work makes lag, offsets, errors and data quality visible, protects production workloads and gives the team clear runbooks for ongoing operation.
Frequently asked questions
What clients ask us most about Change Data Capture — answered in short.
Change Data Capture captures database changes and sends them to other systems as they happen. Companies use CDC for analytics ingestion, application integration, database migration, replication and event-driven workflows without repeatedly scanning entire tables.
CDC moves only changed records and can provide much fresher data than scheduled batch extraction. Compared with traditional replication, it can publish changes to many different consumers and formats, but it requires careful handling of ordering, schemas, retries and delivery guarantees.
Change Data Capture work often involves Debezium, Kafka Connect, Apache Kafka, AWS DMS, Oracle GoldenGate or comparable services. Useful adjacent skills include SQL, source database internals, stream processing, cloud infrastructure, schema management, observability and data quality testing.
CDC is not difficult only because of connector setup. The specialist should have handled production concerns such as transaction boundaries, initial snapshots, deletes, schema evolution, connector recovery and back-pressure in an environment comparable to yours.
Change Data Capture projects can usually be delivered remotely when access, documentation and test data are available. On-site collaboration may help when teams must inspect protected infrastructure, coordinate a migration window or align closely with German-speaking stakeholders.
CDC quality is visible in how the design proves completeness, preserves required ordering and recovers from interruptions. Ask for clear monitoring of lag, offsets, failed records and schema changes, plus documented replay, reconciliation and rollback procedures.
Change Data Capture can overload a source database, duplicate events or silently miss changes if logging and offsets are configured poorly. Discuss source limitations, delete handling, retention, sensitive fields, failure recovery and how downstream consumers will respond to schema changes.
CDC specialists need to know the source database engines, target systems, expected freshness, data volumes, transaction patterns and security constraints. They also need details about existing batch jobs, migration goals, schema ownership and the operational team that will support the pipelines.
The average hourly rate of freelancers in Germany who have used Change Data Capture in their recent projects is 94 €, which corresponds to a daily rate of about 749 € based on an 8-hour working day.
Of the freelancers in Germany who have used Change Data Capture in their recent projects, 93% hold at least a Bachelor's degree and 36% hold at least a Master's degree.
On average, freelancers in Germany who have used Change Data Capture in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Germany who have used Change Data Capture in their recent projects are German (93%), English (93%), and French (14%).
The most common industries among freelancers in Germany who have used Change Data Capture in their recent projects are Information Technology (100%), Automotive (57%), and Banking and Finance (57%).
The most common business areas among freelancers in Germany who have used Change Data Capture in their recent projects are Information Technology (100%), Business Intelligence (79%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Change Data Capture
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 Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
