Apache Airflow Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Apache Airflow
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
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.
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
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
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
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
Kevin M.
Last position:
Freelance Lecturer in Coaching at DSI Education GmbH
- Practice-oriented coaching on core aspects of data science
- Teaching advanced concepts in Python as well as automation (with Make and n8n) and ETL processes with Apache Airflow
- Weekly preparation and delivery of practice-oriented programming courses using real-world examples
- Promoting practical programming skills among participants through interactive exercises and individual support
- Developing didactic materials and adapting content to participants' skill levels
- Close collaboration with the team for continuous improvement of course quality and learning outcomes
Ingo D.
Last position:
Analytics at BaFin - Federal Financial Supervisory Authority Frankfurt
- Introduction of approaches for developing and automatically deploying cloud-native software and machine learning programs in OpenShift clusters
- Development of various programs in Python
- Technologies: Kubernetes, OpenShift, Kustomize, ArgoCD, Tekton, Docker, PodMan, Airflow, IntelliJ, PyCharm, Git, Bitbucket, Jira, Confluence, Python
Discover over 15,000 top freelancers
Statistics of experts using Apache Airflow
Aggregated from the professional profiles of matched freelancers.
Experience
20 years (Germany: 14 years)
Position duration
1.9 years
Positions per freelancer
16 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Government and Administration
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
40% (Germany: 71%)
Doctorate
10%
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 97%)
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 Apache Airflow
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.
Apache Airflow 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%)
- Banking and Finance (67%)
- Government and Administration (50%)
- Retail (50%)
- Telecommunication (50%)
- Pharmaceutical (42%)
- Automotive (33%)
- Energy (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Workflow orchestration
Apache Airflow is an open-source platform for authoring, scheduling and monitoring workflows. Teams define processes as Python-based directed acyclic graphs, or DAGs, with clear task dependencies and execution rules. It is designed for batch-oriented data workflows rather than continuous event processing.
Data pipeline use cases
Companies use Airflow to coordinate ingestion, transformation and delivery across data estates. Typical work includes:
- Scheduling warehouse and lakehouse loads
- Coordinating ELT jobs and quality checks
- Managing machine learning data preparation
- Triggering reports, exports and operational workflows
Airflow can make complex dependencies visible and provide a shared view of pipeline status.
Ecosystem and tooling
Airflow connects with databases, object storage, warehouses and cloud services through provider packages and hooks. Specialists work with Python, SQL, REST APIs, Docker and Kubernetes, while tools such as dbt, Spark and cloud-native storage often sit beside it. They also configure secrets, logging, alerting and deployment workflows.
When expertise matters
Freelance expertise is useful when pipelines have become difficult to operate, migrations require careful planning or a team needs to establish reliable orchestration practices. Signs include repeated manual reruns, unclear ownership, slow DAG parsing, weak failure handling or missing lineage. In Frankfurt, specialists may support local data, finance, logistics and industrial teams remotely or alongside an on-site group.
Delivery and operations
Strong professionals design small, testable DAGs with explicit dependencies and clear retry behavior. They separate configuration from business logic, manage connections securely and make schedules understandable to operators. They also set up deployment, environment promotion, backfills, SLAs, observability and incident procedures rather than treating orchestration as a collection of scripts.
Choosing a specialist
Look for practical evidence of production Airflow work, not only familiarity with its interface. A capable specialist can explain scheduler behavior, executor choices, idempotency, data quality and recovery paths in plain terms. Ask how they would document a pipeline, test a change, control costs and collaborate with data, analytics and infrastructure teams. For Frankfurt projects, agree early on remote routines, on-site needs and the working language.
Frequently asked questions
What clients ask us most about Apache Airflow — answered in short.
Apache Airflow is used to define, schedule and monitor workflows made up of dependent tasks. Companies commonly use it for data ingestion, warehouse loading, transformation, reporting and machine learning preparation.
Apache Airflow offers broad control over workflow code, providers, deployment and execution environments. Managed services can reduce operational work, while Airflow is often preferred when teams need portability, extensive integrations or control over infrastructure.
A strong Airflow specialist usually works comfortably with Python, SQL, APIs, data warehouses and cloud storage. Experience with Docker, Kubernetes, Spark, dbt, CI/CD, secrets management and observability is valuable when the workflow runs in a wider data platform.
The right level depends on the scope. A simple DAG may need focused implementation support, while a shared platform with many teams requires experience with scheduling behavior, executors, upgrades, security, testing and operational recovery.
Yes. Apache Airflow work is well suited to remote collaboration when repositories, environments, credentials and deployment processes are documented. Frankfurt companies should agree on overlap hours, communication routines, language expectations and any need for on-site workshops.
Ask for examples of production pipelines and discuss how failures, retries, backfills and data quality are handled. A reliable Airflow freelancer should explain trade-offs clearly and show how DAGs, alerts, tests and operational documentation are maintained.
Apache Airflow coordinates tasks but is not usually the engine that performs large-scale transformation. It can trigger Spark, dbt, SQL or Python workloads and manage their dependencies, while those systems carry out the processing.
A maintainable DAG has clear ownership, explicit dependencies, safe retries and predictable scheduling. It keeps business logic in suitable processing tools, avoids unnecessary coupling, uses version control and includes tests, alerting and documentation for operators.
The average hourly rate of freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects is 92 €, which corresponds to a daily rate of about 733 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects, 100% hold at least a Bachelor's degree, 40% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects are Information Technology (100%), Banking and Finance (67%), and Government and Administration (50%).
The most common business areas among freelancers in Frankfurt, Germany who have used Apache Airflow in their recent projects are Information Technology (100%), Business Intelligence (92%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Apache Airflow
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 
Munich 
Cologne 
Nuremberg Request a free demo
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FRATCH CEO
