Apache Airflow Experts in Nuremberg
, matched with vetted talent in minutesHire experts who design resilient DAGs, connect data platforms through Airflow providers and improve scheduling, monitoring and recovery. FRATCH matches you quickly and precisely with vetted, available freelancers for remote or on-site work.
Meet FRATCH Experts in Nuremberg, who have recently used Apache Airflow
Elnazossadat H.
Last position:
Data Analyst at Siemens Healthineers
- Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
- Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
- Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
- Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
- Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
- Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
- Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
- Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Arun Sai T.
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
Partha N.
Last position:
AI Software Developer at Fraunhofer IIS
- Built a custom AI chatbot for an e-commerce client using GPT-4 and LangChain with RAG, reducing customer support ticket volume by 45% and improving response accuracy to 92%.
- Designed and deployed an intelligent document processing system using LlamaIndex, Pinecone, and FastAPI for a FinTech startup, enabling semantic search across 100K+ financial documents.
- Developed multi-agent AI workflows using CrewAI and LangGraph for a marketing agency, automating lead research, content generation, and outreach — saving 20+ hours/week of manual work.
- Created AI-powered automation pipelines using n8n, Make, and Zapier integrated with CRMs (GoHighLevel, HubSpot), reducing manual data entry by 80% for a real estate firm.
- Delivered prompt engineering and LLM fine-tuning consulting for multiple clients, optimizing AI model outputs for customer support, content creation, and data extraction use cases.
- Built production-ready REST APIs with Python and FastAPI to serve AI models on AWS and GCP, handling 10K+ daily requests with 99.9% uptime.
Guino N.
Last position:
Senior Data Engineer at Infomotion
- Building a Data Analytics platform for Karl Storz
- Generic development of all ETL processes
- Preparing and providing data in Databricks Delta tables for reuse in Databricks Machine Learning
- Technologies & Tools: Azure Data Factory, CI/CD Pipeline with GitHub DevOps, Python, Azure Databricks (Unity Catalog), T-SQL
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Vasuraj B.
Last position:
Cloud Data Analyst at Bhatia Reply
- Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
- Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
- Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
- Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
- Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker
Discover over 15,000 top freelancers
Statistics of experts using Apache Airflow
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 14 years)
Position duration
2.2 years (Germany: 1.9 years)
Positions per freelancer
6 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
71%
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Hindi
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 Nuremberg 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 Nuremberg 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 (71%)
- Automotive (57%)
- Healthcare (43%)
- Retail (43%)
- Education (29%)
- Energy (29%)
- Manufacturing (29%)
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 pipelines as Python-based DAGs, then use dependencies, retries and schedules to coordinate data movement, transformation and operational tasks. It is built for workflows that need clear visibility and controlled execution rather than simple one-off scripts.
Data pipeline delivery
Airflow specialists create pipelines that move and prepare data across warehouses, lakes, APIs and business systems. Common deliverables include ingestion workflows, transformation orchestration, backfills, dependency management and operational alerts. They also help turn fragile cron jobs into observable, maintainable processes.
- Design DAGs with clear task dependencies
- Schedule batch and event-driven workflows
- Add retries, timeouts and failure handling
- Coordinate transformations with dbt or Spark
Ecosystem and tooling
The Airflow ecosystem includes providers for cloud services, databases, messaging systems and storage technologies. Strong specialists work with the scheduler, workers, metadata database, web interface and executor choices, including CeleryExecutor and KubernetesExecutor. They may also bring experience with Docker, Kubernetes, Terraform, Git, CI/CD and data quality tools.
When expertise matters
Companies bring in freelance Apache Airflow expertise when a pipeline migration, platform rollout or reliability problem needs focused delivery. Specialist support is useful when DAGs are slow to maintain, failures are hard to diagnose or scheduling logic has outgrown existing scripts. In Nuremberg, teams may combine remote collaboration with on-site workshops, depending on security and communication needs.
- Standardize pipeline patterns across teams
- Improve observability and incident response
- Migrate workflows from cron or legacy schedulers
- Prepare Airflow for production operations
Quality signals
A strong professional separates orchestration from business logic and keeps DAGs readable, testable and idempotent. They understand task queues, concurrency, resource limits, secrets, access control and deployment environments. They also document ownership, recovery steps and data dependencies so the workflow remains useful after handover.
Choosing a specialist
Review examples of production DAGs, migration work and integrations with the systems in your stack. Ask how the specialist handles retries, backfills, schema changes, late data and failed downstream tasks. For remote work, confirm availability during operational windows and the language needed for documentation, workshops and incident communication.
Frequently asked questions
Quick answers to the questions that come up most around Apache Airflow.
Apache Airflow is used to define, schedule and monitor workflows with dependencies between tasks. Companies commonly use it for batch data pipelines, warehouse loads, analytics preparation, machine learning workflows and recurring operational processes.
Apache Airflow provides a visual workflow view, dependency handling, retries, logging and operational controls that basic cron jobs do not offer. It is usually considered alongside Prefect, Dagster and managed cloud orchestration services, with the best choice depending on team preferences, deployment needs and workflow complexity.
A strong Apache Airflow specialist often works with Python, SQL, cloud storage, data warehouses and transformation tools such as dbt. Experience with Docker, Kubernetes, Terraform, CI/CD, secrets management and data quality practices is also valuable for production delivery.
An Airflow freelancer should be able to assess the current workflows, define a maintainable DAG structure and deliver reliable scheduling and monitoring. The required depth depends on whether the project involves a few pipelines, a migration, or a shared platform with strict operational requirements.
Apache Airflow is well suited to remote collaboration because DAGs, configuration and deployment practices can be reviewed in shared repositories and environments. Teams in Nuremberg should agree on documentation standards, meeting language, access procedures and availability for workflow incidents before work begins.
Apache Airflow may be unsuitable for low-latency stream processing, simple single-step schedules or workflows that need immediate event reactions. A streaming engine, lightweight scheduler or event-driven service can be a better choice when continuous processing and very low response times are central.
Look for Apache Airflow workflows with clear task boundaries, safe retries, idempotent operations, useful alerts and tested recovery paths. Ask the specialist to explain backfills, dependency changes, secrets, access control and how failures are investigated in production.
Before changing Apache Airflow, a freelancer should understand the executor, deployment model, metadata database, provider integrations and data ownership. They should also review current DAG conventions, operational pain points, security requirements and the process for testing and releasing workflow changes.
The average hourly rate of freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects is 75 €, which corresponds to a daily rate of about 601 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects, 100% hold at least a Bachelor's degree and 71% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects are German (100%), English (100%), and Hindi (29%).
The most common industries among freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects are Information Technology (100%), Banking and Finance (71%), and Automotive (57%).
The most common business areas among freelancers in Nuremberg, Germany who have used Apache Airflow in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (86%).
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 
Frankfurt Request a free demo
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