
Data Pipeline Experts in Frankfurt
, matched in minutes from over 15,000 CVs by AIHire experts who design ETL and ELT workflows, connect cloud data platforms, and automate reliable processing for analytics and machine learning. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Frankfurt, who have recently used Data Pipeline
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
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.
Eduard V.
Last position:
Lead of workshop “Introduction to AI Development Tools” at Software company in Wiesbaden
- Presentation introducing generic A.I. and Large Language Models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, DSGVO)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based solutions, as well as change management and works council considerations
- Moderation of the discussion and definition of next steps for introducing AI development tools
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
Ralitsa P.
Last position:
Process, Project and Requirements Manager, Customer Project @ RUBINLAKE at 1&1 Versatel GmbH
- Introduction of a new workflow tool for Fibre Connectivity & Broadband Delivery
- Requirements design, estimation and prioritization
- Creation of rollout and project plans
- Management of the development team according to Scrum
- Design and project management of the API interface to existing tools
- Process modeling for the new Order Dispatching team (FCD & BD)
- Design of processes within and outside the team
- Requirements design and implementation of the necessary tools
- Creation and calculation of production KPIs
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.
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
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
Discover over 15,000 top freelancers
Statistics of experts using Data Pipeline
Aggregated from the professional profiles of matched freelancers.
Experience
14 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: 99%)
Master's degree or higher
71% (Germany: 72%)
Doctorate
18% (Germany: 12%)

Certifications per freelancer
4 (Germany: 3)

Most common languages
German, English, French

Speak two or more languages
100% (Germany: 98%)
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 Frankfurt 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 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 29 Oct 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 (50%)
- Healthcare (45%)
- Transportation (45%)
- Energy (36%)
- Professional Services (36%)
- Retail (36%)
- Automotive (32%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data Flow Foundations
A data pipeline moves information from source systems to destinations where it can be analyzed, operationalized, or used by applications. It may support batch processing, real-time streaming, or a combination of both. Strong designs preserve data quality, lineage, security, and reliable delivery.
Typical Deliverables
- Extract data from APIs, databases, files, and event streams
- Transform, validate, enrich, and standardize records
- Load curated data into warehouses, lakes, and operational stores
- Orchestrate dependencies, retries, alerts, and scheduled runs
These workflows power reporting, customer analytics, fraud detection, recommendation systems, and machine learning features. The right architecture depends on data volume, latency, source reliability, and governance needs.
Ecosystem And Tools
Professionals may work with Apache Airflow, Dagster, Prefect, dbt, Apache Kafka, Spark, and cloud-native services. Common storage and query environments include Snowflake, BigQuery, Redshift, Databricks, and cloud object storage. SQL and Python often sit alongside APIs, containers, infrastructure as code, and observability tools.
When To Bring Expertise
Companies often need freelance support when a pipeline has become difficult to maintain, reporting data cannot be trusted, or a migration is changing source and target systems. Frankfurt teams in finance, logistics, manufacturing, retail, and software may also need specialists who can collaborate on-site when useful while delivering remotely across distributed teams.
- Replace fragile scripts with tested workflows
- Reduce delays between operational events and analysis
- Reconcile records across disconnected systems
- Prepare pipelines for cloud migration or platform consolidation
Skills That Matter
A capable specialist understands source behavior, schema evolution, idempotency, partitioning, incremental loading, and failure recovery. They test transformations, document lineage, control access, and monitor freshness, volume, and accuracy. Experience with ETL and ELT helps them choose a practical design rather than forcing every workload into one pattern.
What Good Delivery Looks Like
Quality is visible in reproducible deployments, clear ownership, useful alerts, and safe recovery procedures. Strong professionals explain trade-offs between batch and streaming, validate assumptions with business stakeholders, and leave maintainable workflows behind. They also account for data protection, cost control, and the language expectations of teams working in Frankfurt or remotely.
Frequently asked questions
Not sure where to start with Data Pipeline? These answers cover the essentials.
A data pipeline transfers and processes information between systems so it can support analytics, reporting, applications, or machine learning. It can collect data from databases, APIs, files, and event streams, then validate, transform, and deliver it to a useful destination.
A data pipeline is the broader workflow for moving and processing data, while ETL and ELT describe where transformation happens in that workflow. ETL transforms before loading into the target, whereas ELT loads first and transforms inside a warehouse or lakehouse.
A data pipeline specialist commonly works with SQL, Python, cloud storage, data warehouses, APIs, and orchestration tools such as Apache Airflow. Depending on the project, useful adjacent skills include Kafka, Spark, dbt, Terraform, Docker, data modeling, testing, and observability.
The right data pipeline experience depends on source complexity, reliability requirements, processing speed, and regulatory constraints. A straightforward batch workflow may need a different specialist than a streaming architecture with schema changes, recovery requirements, and multiple cloud services.
A data pipeline project can usually be delivered remotely when documentation, access controls, and communication routines are clear. On-site collaboration in Frankfurt can help during discovery, source-system workshops, or handover, while many implementation tasks work well across distributed teams.
Ask how the data pipeline specialist handles failed jobs, duplicate records, schema evolution, backfills, and sensitive data. Review examples of monitoring, tests, deployment practices, and documentation rather than judging only the choice of tools.
A data pipeline should use streaming when decisions depend on events arriving with low delay, such as alerts, operational monitoring, or real-time personalization. Batch processing is often simpler and more economical when data can be handled on a schedule without immediate action.
A strong data pipeline freelancer demonstrates repeatable deployments, automated tests, lineage, freshness checks, useful alerts, and documented recovery steps. They should also explain how the workflow prevents silent data loss and how teams can verify outputs after changes.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects is 91 €, which corresponds to a daily rate of about 727 € 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, 71% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Pipeline in their recent projects have 14 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 (32%).
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 (50%), and Healthcare (45%).
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 (77%).
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.
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