Apache Airflow Experts in Vienna
to orchestrate reliable data workflows with fast, precise AI matchingHire experts who design DAGs, connect data warehouses and cloud services, and improve scheduling, monitoring and recovery across production pipelines. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Vienna, who have recently used Apache Airflow
Manuel P.
Last position:
AI Engineer at Misumi Europe GmbH & Motius GmbH
- Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
- Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
- Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
- Optimized LLM based data extraction and classification with context engineering.
- Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Anton L.
Last position:
Self-employed at Controlling Lab eU
Business intelligence and digitalization projects
Controlling as a Service
Financial accounting and payroll accounting
Interim management
Business Intelligence Competence Team (2025-2026) at a food retailer (21 Bn. revenue, 52k employees): Project work at the interface between the finance department and IT service provider; various implementation projects in reporting and business intelligence
Consulting services (2025-2026) for a biotech startup (3 Mio. revenue, 20 employees): Optimization and automation of accounting and controlling processes; introduction of a central data warehouse system for planning and reporting
Consulting services (2025-2026) in the broadcasting and telecommunications industry (130 Mio. revenue, 200 employees): Analysis and troubleshooting of financial processes in the BSS system (customer management and billing system)
Consulting services (2024) in the broadcasting and telecommunications industry (130 Mio. revenue, 200 employees): Migration and implementation of cost accounting in a new database system
Consulting services (2025-2026) for advertising/marketing agencies (40 Mio. revenue, 450 employees): Establishment of group-wide cash management in the holding company, liquidity planning, receivables management and consolidation
Kevin L.
Last position:
Data Consultant at VBV Pensions- und Vorsorgekasse Österreich
Development of a structured framework and comprehensive guidelines for documenting business and audit processes in a regulated financial environment. Support in standardizing process documentation to improve transparency, consistency, and traceability across all operational workflows. Contribution to defining documentation standards, templates, and governance principles for internal process management and audit readiness.
Discover over 15,000 top freelancers
Statistics of experts using Apache Airflow
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.8 years
Positions per freelancer
10
Top business areas
Business Intelligence, Information Technology, Strategy
Top industries
Information Technology, Manufacturing, Media and Entertainment
Certification focus areas
Information Technology, Business Intelligence, Accounting
Bachelor's degree or higher
100%
Master's degree or higher
100%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
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 Vienna 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 Vienna 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 (67%)
- Manufacturing (67%)
- Media and Entertainment (67%)
- Banking and Finance (50%)
- Retail (50%)
- Biotechnology (33%)
- Education (33%)
- Healthcare (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 batch-oriented workflows. Teams define pipelines as Python code and represent dependencies as directed acyclic graphs, or DAGs. It coordinates data ingestion, transformation, validation and delivery without embedding the business logic of every task in one system.
Core building blocks
Airflow workflows use DAGs, tasks, operators, sensors, hooks and connections. The scheduler creates task instances, workers execute them, and the metadata database records state for the web interface and operational checks. Strong specialists also configure retries, backfills, time zones, concurrency limits and alerting for dependable runs.
Ecosystem and tooling
Airflow commonly connects with cloud storage, relational databases, data warehouses, Kubernetes and messaging services. Its provider packages integrate systems such as Amazon Web Services, Google Cloud, Microsoft Azure, Snowflake, BigQuery, dbt and Apache Spark. Professionals often manage environments with Docker, Helm, Terraform and continuous integration pipelines.
Where companies use it
- Schedule ingestion from APIs, applications and operational databases
- Coordinate warehouse and lakehouse transformations
- Run quality checks before publishing trusted datasets
- Orchestrate machine learning training and batch scoring
- Trigger reports, exports and downstream business processes
Airflow suits workflows that need clear dependencies, auditability and operational visibility. It is less suited to low-latency event processing, where streaming tools may be a better fit.
When expertise matters
Companies bring in freelance expertise when existing pipelines are unreliable, a legacy scheduler needs replacement or a new data platform must be made operational. Specialists can establish conventions for DAG design, secrets, environments, testing and deployment. In Vienna, remote collaboration is common, while on-site work can help with workshops, migration planning and stakeholder alignment.
What strong specialists deliver
The best professionals treat orchestration as a production system, not just a collection of scripts. They make tasks idempotent, separate configuration from code, control retries and prevent unnecessary reruns. They also explain failure modes clearly, document ownership and use logs, metrics and alerts to shorten recovery. Experience with data modeling, SQL, Python, cloud infrastructure and security makes their decisions more complete.
Frequently asked questions
Not sure where to start with Apache Airflow? These answers cover the essentials.
Apache Airflow is used to schedule and monitor workflows with dependencies between tasks. Companies use it for data ingestion, warehouse transformations, quality checks, reporting, machine learning pipelines and other repeatable batch processes.
Apache Airflow is a strong choice when teams want Python-defined workflows, a broad integration ecosystem and a visible scheduling model. Tools such as Dagster, Prefect, Argo Workflows and cloud-native schedulers may be preferable when a team prioritizes a different execution model, tighter Kubernetes integration or a more opinionated developer experience.
A capable Apache Airflow specialist should also understand Python, SQL, data modeling and production operations. Cloud services, Docker, Kubernetes, Terraform, CI/CD, warehouse technology and observability are valuable adjacent skills, depending on the environment.
The right level depends on the workflow complexity, reliability requirements and existing platform. A focused DAG improvement may need a specialist who can work within established patterns, while a migration or production rollout calls for experience with architecture, deployment, security and incident handling.
Yes. Apache Airflow work is often well suited to remote collaboration because code, configuration, logs and deployments can be reviewed online. Vienna teams should agree on working hours, documentation standards and language expectations early, and use on-site sessions when workshops or access constraints make them useful.
Apache Airflow is not designed to be a low-latency stream processor or a general-purpose message broker. For continuous event handling, teams may need streaming technologies, while simple isolated schedules may be easier to operate with a lighter scheduler.
Ask how the Apache Airflow professional handles idempotency, retries, backfills, dependency failures, secrets and observability. Review examples of DAG structure and deployment practices, then discuss how they test workflows and investigate a failed production run.
Before working with Apache Airflow, clarify the scheduler and executor setup, provider integrations, deployment process and ownership of failures. Freelancers should also understand data sensitivity, cloud permissions, release controls and whether the team expects remote delivery or collaboration with colleagues in Vienna.
The average hourly rate of freelancers in Vienna, Austria who have used Apache Airflow in their recent projects is 104 €, which corresponds to a daily rate of about 829 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Apache Airflow in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Vienna, Austria who have used Apache Airflow in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Vienna, Austria who have used Apache Airflow in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Vienna, Austria who have used Apache Airflow in their recent projects are Information Technology (67%), Manufacturing (67%), and Media and Entertainment (67%).
The most common business areas among freelancers in Vienna, Austria who have used Apache Airflow in their recent projects are Business Intelligence (100%), Information Technology (100%), and Strategy (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.
Countries:
Vienna 
Graz Request a free demo
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Philipp Thomaschewski
FRATCH CEO
