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Apache Airflow Experts

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Hire experts who orchestrate ETL and ELT pipelines, connect cloud data platforms, and manage complex workflow dependencies with Apache Airflow, Python, and providers across modern data stacks. FRATCH matches you quickly with vetted, available freelancers who fit your project.

Meet FRATCH Experts who have recently used Apache Airflow

Verified expert

Patrick L.

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Senior AI Software Engineer with 9 years of experience delivering practical AI products for enterprise and public sector

Frankfurt am Main
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

Verified expert

Collin K.

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Lead Fullstack Developer

Kempen
Collin K.

Last position:

Software Architect / Fullstack Developer at Equity Bytes

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

  • Designed and operated a highly scalable microservice and serverless architecture
  • Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
  • Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
  • Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
  • End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
  • Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
  • Robust backup and disaster recovery strategies
  • RDS Postgres backups and hourly snapshots
  • Read-only, asynchronously synchronized replicas with automated master failover in emergencies
  • Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
  • Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
  • Integrated Stripe for international payment processing
  • Built a marketplace payment system with multiple parties and payout routines
  • Used Algolia for high-performance real-time search of digital assets on the platform
  • Federation of services with GraphQL and Hasura
  • Later migration to GraphQL Mesh
  • Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
  • Used Next.js / React for modern frontend applications in the nx monorepo
  • Enterprise security architecture & access control
  • Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
  • Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
  • Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

Verified expert

Ali A.

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Enterprise Software Architect | Payments, Cloud & AI Platforms

Frankfurt
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
Verified expert

Mirza K.

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Agentic AI for a DeepResearch project

München
Mirza K.

Last position:

Agentic Automation and a RAG system

  • This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

Verified expert

Karin A.

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Language Expert – Python Developer – AI Engineer

Leonberg
Karin A.

Last position:

AI Benchmark Engineer | Native language specialist German at Lilt

  • Task Engineering: Evaluating Coding Agents.
  • Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
  • Prompting & Translation: finding failure points where AI does not work, in German.
  • Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
  • Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
  • Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
  • Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Verified expert

Bidya B.

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Senior Product Owner - Agentic AI & Intelligent Platforms | Fintech, SaaS, Risk & Ecommerce

Berlin
Bidya B.

Last position:

Product Manager – Payments & Platform at Pipedrive

CRM and revenue platform managing billing and subscription workflows.

  • Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
  • Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
  • Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
  • Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Verified expert

Oleg A.

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Staff Software Engineer

Berlin
Oleg A.

Last position:

Staff Software Engineer at Kpler Germany GmbH

  • Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
  • Collaborating with other teams to integrate more domains

Tech stack:

  • Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
  • BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
  • Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Verified expert

Daryoosh D.

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Enterprise Data & AI Architect

Offenburg
Daryoosh D.

Last position:

FP&A Data & AI Architect at Epta Group

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

  • Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
  • Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
  • Validated SAP reports, establishing baseline data quality standards for Finance team consumption
  • Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

  • Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
  • Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
  • Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
  • Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

  • Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
  • Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
  • Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

  • Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
  • Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
  • Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

  • GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
  • Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
  • Power BI transformation roadmap presented and approved by Finance leadership
  • Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

Verified expert

Alexander Z.

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Senior Data Architect & Data Engineer

Berlin
Alexander Z.

Last position:

Senior Data Solutions Engineer at VMware Inc.

  • Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
  • Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
  • Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
  • Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Verified expert

Philipp G.

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Machine Learning & Data Engineer

München
Philipp G.

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

  • Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
  • Implementation of automated end-to-end cloud processes
  • Development of LLM and NLP models
  • Creation of interactive reports
  • Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Verified expert

Alexander B.

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Senior Data Engineer

Köln
Alexander B.

Last position:

Senior Data Engineer at RWE AG

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

Verified expert

Nitin B.

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Data & Analytics Leader

Berlin
Nitin B.

Last position:

Financial Analytics Lead at Independent Consultant

Led FP&A tech transformation for a 9-figure business – from resolving legacy technical debt to leading AI-native EPM implementation

  • Driving end-to-end FP&A transformation, from architecture redesign through EPM tool selection to rollout
  • Ran evaluation of 12+ EPM platforms, from vendor negotiation to selection framework tied to long-term planning
  • Diagnosed constraints in financial planning architecture, presented findings to the CFO, and secured executive mandate to redesign FP&A infrastructure from the ground up
Verified expert

Samuel K.

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Agentic AI Engineer & Technical Lead

Ingolstadt
Samuel K.

Last position:

Founder & Agentic AI Engineer at Agentakt LLC

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

  • Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

  • Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

  • Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

Verified expert

Laurin H.

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Software Architect (Freelance)

Bochum
Laurin H.

Last position:

Software Architect (Freelance) at Care4Sure

  • Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
  • Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

Discover over 15,000 top freelancers

Statistics of experts using Apache Airflow

Aggregated from the professional profiles of matched freelancers.

Experience

14 years

Apache Airflow experts have 14 years of professional experience on average.

Position duration

1.9 years

Apache Airflow experts stay in a single position for 1.9 years on average.

Positions per freelancer

10

Apache Airflow experts have completed 10 positions on average over the course of their careers.

Top business areas

Information Technology, Business Intelligence, Product Development

Apache Airflow experts have gathered most of their hands-on project experience in Information Technology, Business Intelligence, and Product Development.

Top industries

Information Technology, Banking and Finance, Automotive

Apache Airflow experts are most in demand in Information Technology, Banking and Finance, and Automotive.

Certification focus areas

Information Technology, Business Intelligence, Product Development

Apache Airflow experts earn their certifications most often in Information Technology, Business Intelligence, and Product Development.

Bachelor's degree or higher

99%

99% of Apache Airflow experts hold at least a Bachelor's degree.

Master's degree or higher

71%

71% of Apache Airflow experts hold at least a Master's degree.

Doctorate

11%

11% of Apache Airflow experts have a doctorate (PhD).

Certifications per freelancer

3

Apache Airflow experts hold 3 professional certifications on average.

Most common languages

English, German, French

Apache Airflow experts most often speak English, German, and French.

Speak two or more languages

97%

97% of Apache Airflow experts speak two or more languages.

Based on our profile pool as of 16 Oct 2026.

Daily rate distribution

0% 25% 50% 75% 100%
8% of Apache Airflow experts charge less than €400 per day.
46% of Apache Airflow experts charge between €400 and €800 per day.
42% of Apache Airflow experts charge between €800 and €1200 per day.
4% of Apache Airflow experts charge between €1200 and €1600 per day.
1% of Apache Airflow experts charge €1600 or more per day.
<€400 €400-​800 €800-​1200 €1200-​1600 €1600+

The chart shows how the daily rates of experts in this technology 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 using Apache Airflow

Rates are based on recent contracts and do not include FRATCH margin.

800
600
400
200
Rate comparison chart
Daily rate avg. 728 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

800
600
400
200
Rate comparison chart
Median rate 760 €

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 16 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 (89%)
  • Banking and Finance (51%)
  • Automotive (42%)
  • Retail (39%)
  • Education (37%)
  • Professional Services (36%)
  • Manufacturing (30%)
  • Healthcare (27%)

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, organize work in directed acyclic graphs, and control dependencies between tasks. It is built for observable, repeatable data operations rather than continuous event streaming.

Data pipeline use cases

  • Schedule ingestion from databases, APIs, files, and SaaS systems
  • Coordinate ELT workflows for warehouses and lakehouses
  • Run validation, transformation, and reconciliation steps
  • Refresh reports, feature datasets, and machine learning inputs
  • Trigger dependent jobs across cloud and on-premises systems

Airflow helps teams make complex data processing visible and manageable. Its scheduler, web interface, logs, retries, alerts, and backfill controls support dependable operations when workflows need regular execution and clear ownership.

Ecosystem and tooling

Airflow pipelines commonly use Python operators, task flow APIs, sensors, hooks, and provider packages. Integrations cover systems such as BigQuery, Snowflake, Amazon Redshift, Google Cloud, Microsoft Azure, Kubernetes, Apache Spark, dbt, and common relational databases. Strong specialists also work with Docker, Terraform, CI/CD, secrets management, and observability tools.

When expertise matters

Companies bring in freelance Airflow specialists when pipelines have become difficult to maintain, scheduling failures affect reporting, or a migration requires careful orchestration. Expertise is also valuable when teams need to standardize DAGs, improve deployment practices, reduce operational noise, or connect new warehouse and cloud services without disrupting existing runs.

  • Replace fragile scripts with maintainable DAGs
  • Design reliable retries, timeouts, and failure handling
  • Tune scheduling, concurrency, pools, and resource use
  • Establish testing, release, and documentation standards

What strong professionals deliver

A capable professional designs clear task boundaries and keeps business logic separate from orchestration code. They understand idempotency, incremental loading, data quality checks, timezone behavior, dependency management, and backfills. They also make workflows understandable to the people who operate and audit them.

Selecting the right specialist

Review examples of production DAGs, incident handling, and integrations similar to your stack. Ask how the specialist would manage retries, late data, schema changes, secrets, and deployment across environments. Good work includes readable code, useful alerts, controlled access, tested recovery paths, and documentation that lets the internal team operate the system confidently.

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Frequently asked questions

Everything clients usually want to know about Apache Airflow, in one place.

Apache Airflow is used to define, schedule, and monitor batch workflows such as data ingestion, warehouse loading, reporting refreshes, and machine learning preparation. It coordinates tasks across different systems and records their status, logs, retries, and dependencies.

Apache Airflow has a broad provider ecosystem and a mature scheduling model built around DAGs and task dependencies. Prefect and Dagster may offer different approaches to workflow definitions, state handling, or developer experience, so the right choice depends on your team, existing stack, and operating model.

An Apache Airflow specialist should usually understand Python, SQL, cloud storage, data warehouses, APIs, containers, and CI/CD. Experience with Kubernetes, Spark, dbt, Terraform, secrets management, and data quality tools is useful when workflows span multiple services.

The right Apache Airflow experience depends on the work, not just the size of the pipeline. A straightforward DAG may need focused workflow knowledge, while a platform migration or production redesign calls for proven skills in deployment, scaling, monitoring, security, and recovery.

Apache Airflow projects can be handled remotely when repositories, environments, access controls, and operational ownership are clearly organized. Teams should agree on documentation, review practices, incident communication, and access to logs and deployment systems before work begins.

Apache Airflow is primarily designed for batch orchestration and scheduled workflows, not continuous event processing. It can trigger or coordinate streaming jobs, but technologies such as Kafka, Flink, or Spark Structured Streaming usually handle the real-time processing itself.

Ask an Apache Airflow professional to explain DAG structure, idempotency, retries, backfills, data delays, testing, and deployment. Strong answers connect technical choices to operational outcomes and show how the workflow remains observable and safe to change.

Before starting an Apache Airflow assignment, clarify the existing version, executor, deployment model, provider packages, data sources, warehouse, and incident process. Freelancers should also confirm who owns credentials, infrastructure, production support, and decisions about pipeline reliability.

The average hourly rate of freelancers who have used Apache Airflow in their recent projects is 91 €, which corresponds to a daily rate of about 728 € based on an 8-hour working day.

Of the freelancers who have used Apache Airflow in their recent projects, 99% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 11% hold a doctorate.

On average, freelancers who have used Apache Airflow in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.9 years.

The most common languages among freelancers who have used Apache Airflow in their recent projects are English (99%), German (96%), and French (20%).

The most common industries among freelancers who have used Apache Airflow in their recent projects are Information Technology (89%), Banking and Finance (51%), and Automotive (42%).

The most common business areas among freelancers who have used Apache Airflow in their recent projects are Information Technology (98%), Business Intelligence (84%), and Product Development (76%).

Main locations of FRATCH Experts, who have recently used Apache Airflow

Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg

In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Vienna Graz

Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Zurich Geneva Basel Bern

Countries:

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