Data Pipeline Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Data Pipeline
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.
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.
Carlbandro E.
Last position:
IT Lecturer / Coach at Freelance
As a freelance IT lecturer and IT coach, I specialize in teaching individuals concepts like Cloud Computing, Business Intelligence, Python Programming and Agile Frameworks. My goal is to simplify complex technical concepts into practical, actionable knowledge. Through my extensive experience as an IT consultant in various sectors and roles I know about the importance of IT training - for companies and employees alike.
Jana C.
Last position:
Freelance Business Intelligence Consultant at Superprof Germany & Upwork
- Design an interactive Tableau dashboard to analyze historical aviation accidents using data from the Federal Bureau of Aircraft Accident Investigation
- Develop visualizations to provide actionable insights to improve aviation safety measures employing data modeling and transformation techniques
- Develop a Power BI dashboard to monitor sales KPIs and product performance integrating data from multiple sources
- Design a star schema data model and implement advanced DAX measures for high-performance and dynamic reporting
- Design a comprehensive BI architecture solution focusing on ETL pipelines, data integration, and data governance
- Recommend Power BI as the central tool and create a security-compliant implementation roadmap
Basil S.
Last position:
Senior Developer / Data Engineer at Large energy-sector company
- Co-founded the Real-Time Data team, which grew to 10 members over time.
- Developed and delivered core data products.
- Optimized real-time application performance and implemented monitoring, alerting and logging solutions to ensure system stability.
- Created and maintained deployment pipelines.
- Collaborated with teammates, architects and experts in an agile Scrum environment.
- Operated applications, analyzed, tested and troubleshot software solutions.
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
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
Anton R.
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Kevin M.
Last position:
Backend & Infrastructure Engineer at Mileo Systems GmbH
- Engineered production-ready Azure environments using Terraform, ensuring consistent infrastructure parity across VNets and Resource Groups
- Implemented Microsoft Fabric tenant and workspace architecture for multi-stage Medallion data processing pipelines
- Designed secure data pathways using Managed Private Endpoints for isolated Azure Storage access
- Managed Service Principals and authentication tokens for secure REST API integrations
Marie-Josée M.
Last position:
Java Engineer / Developer at Davaso GmbH
- Involved in the full development lifecycle, including design, coding, testing, and deployment of Java-based applications.
- Built and maintained high-performance RESTful APIs and microservices, ensuring scalability and reliable data exchange.
- Practiced code reviews, used version control systems (e.g., Git), and collaborated closely with cross-functional teams to deliver robust software solutions.
- Implemented, managed, and optimized complex business logic and decision-making processes using the Drools Rules Engine, leveraging DRL (Drools Rule Language) for dynamic configuration.
- Developed software solutions for a major service provider in the healthcare insurance sector, specifically focusing on claims processing and reconciliation logic to ensure cost-effective reimbursement for health funds.
Eduard V.
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Jonas W.
Last position:
Fullstack Developer at Soulven Agency
- Working for customer and internal projects ranging from B2C, B2B applications
- Development and maintenance of customer-facing and internal applications across B2C and B2B domains
- Responsibility for technical direction, architectural decisions and code quality standards
- Design and implementation of scalable application architectures, including modular frontend and service-oriented backend structures
- Conceptual planning and technical execution of the agency website relaunch in Angular
- Rapid prototyping and MVP development for B2B customer projects in the pharmaceutical sector with Vue.js
- Creation of first MVPs for 3D rendering–based products, including real-time visualization and interactive experiences
- Strong UX/UI expertise with a focus on gamification, user engagement and conversion-driven interfaces
- Close collaboration with product management, design and stakeholders to translate business requirements into technical solutions
- Agile software development using Scrum, including sprint planning, reviews and retrospectives, supported by Azure DevOps
- Full-stack development using React, Angular, TypeScript, Vue.js, HTML5, CSS3/SCSS, C#, .NET, Node.js, RESTful APIs, Unity, Azure DevOps, Git and CI/CD pipelines
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Delly F.
Last position:
Dad of 2 daughters at Family
Discover over 15,000 top freelancers
Statistics of experts using Data Pipeline
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 13 years)
Position duration
2.1 years (Germany: 2.8 years)
Positions per freelancer
11 (Germany: 8)
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
100% (Germany: 98%)
Master's degree or higher
69% (Germany: 71%)
Doctorate
13%
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 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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Pipeline 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 (86%)
- Banking and Finance (48%)
- Healthcare (48%)
- Transportation (48%)
- Energy (38%)
- Automotive (33%)
- Manufacturing (33%)
- Professional Services (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Pipelines Do
A data pipeline moves information from source systems to destinations where teams can analyse it, trigger processes or power products. It can ingest application events, transactions, files, APIs and streaming data, then validate, transform and deliver it to warehouses, lakes or operational systems. Reliable pipelines make data timely, traceable and usable.
Core Pipeline Patterns
Batch workflows process data on a schedule, while streaming pipelines handle events as they arrive. Strong designs define schemas, manage dependencies and support safe reprocessing when a source changes or a job fails.
- Extract data from databases, APIs, files and event streams
- Transform, enrich, validate and standardise records
- Load data into warehouses, lakehouses and services
- Monitor freshness, failures, lineage and data quality
Ecosystem and Tooling
The right specialists work across orchestration, storage, processing and observability. Common ecosystems include Apache Airflow, Dagster, Kafka, Spark, dbt, Snowflake, BigQuery, Databricks and cloud services from AWS, Azure or Google Cloud. They also use Python, SQL, Docker, Terraform and CI/CD practices to make pipelines repeatable and maintainable.
When Companies Need Support
Companies often bring in freelance expertise during a warehouse migration, a streaming rollout or a wider analytics modernisation. A specialist can replace fragile scripts, connect siloed systems and establish dependable deployment and monitoring practices. Frankfurt teams may also value professionals who can collaborate remotely while joining on-site workshops when architecture or stakeholder alignment requires it.
- Data arrives late, inconsistently or without clear ownership
- Manual exports and scripts slow down reporting
- A new warehouse, lakehouse or event platform needs integration
- Pipeline failures are difficult to detect or recover from
Skills Behind Reliable Flows
Quality work combines data modelling, distributed processing and software engineering. Professionals should understand idempotency, partitioning, schema evolution, access control, testing and recovery. They also need to explain trade-offs between latency, cost, complexity and operational risk to both technical and business stakeholders.
Choosing the Right Specialist
Review delivered pipeline designs, not only tool names on a profile. Ask how the professional handles bad records, changing schemas, backfills, sensitive data and incident response. A strong specialist can show clear documentation, meaningful tests, observable jobs and a practical rollout plan that fits your existing stack and team.
Frequently asked questions
Not sure where to start with Data Pipeline? These answers cover the essentials.
A data pipeline transfers data between systems and prepares it for reporting, analytics, machine learning or operational use. It can combine batch processing with real-time events and apply validation, enrichment, masking and quality checks along the way.
A data pipeline is the broader flow of moving and processing data, while ETL and ELT describe where transformation occurs. ETL transforms before loading, whereas ELT loads raw data first and transforms it in the target warehouse or lakehouse.
A data pipeline specialist should usually be comfortable with SQL, Python, data modelling, cloud storage and orchestration. Kafka, Spark, dbt, Terraform, Docker, CI/CD, security and observability are also useful when the work spans modern analytics platforms.
The right data pipeline experience depends on source complexity, delivery speed, data criticality and operational expectations. A straightforward warehouse load may need focused implementation skills, while regulated or streaming environments call for strong architecture, testing and incident-recovery experience.
Yes, data pipeline work is often well suited to remote collaboration through repositories, cloud environments, tickets and documentation. Frankfurt-based companies should define access controls, meeting language and any need for on-site workshops early, especially when several teams own connected systems.
Ask a data pipeline professional to explain lineage, failure handling, idempotency, schema changes and backfills in a relevant scenario. Look for observable jobs, automated tests, clear runbooks and sensible trade-offs rather than familiarity with tools alone.
A data pipeline should use streaming when decisions or product features depend on low-latency events, such as alerts or live recommendations. Batch is often simpler and more economical for periodic reporting, large historical loads and processes without immediate delivery needs.
A data pipeline freelancer should deliver configured workflows, source and target mappings, transformation logic, tests and deployment instructions. Useful handover material also includes monitoring dashboards, alert rules, recovery procedures, lineage notes and documentation for future changes.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects is 89 €, which corresponds to a daily rate of about 716 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects, 100% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects are German (100%), English (100%), and French (29%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects are Information Technology (86%), Banking and Finance (48%), and Healthcare (48%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (76%).
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 
Cologne 
Dusseldorf 
Essen 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
