Hire Apache Airflow developers

Quickly automate complex workflows. Apache Airflow devs streamline data pipelines and task scheduling—hire now, onboard this week.

Faisal
Data Engineer
Singapore/(GMT +7:00)
Faisal is a seasoned engineer with extensive proficiency in SQL, Python, and AWS services, demonstrating a solid understanding of complex data queries and varied approaches. His proficiency as an efficient communicator shines through in his ability to deliver clear, concise responses and accurately articulate complex technical concepts. With strong analytical abilities and knowledge, he is primed for success in any Python-heavy role, adapting seamlessly to diverse technical environments.
Experience
9 years
Availability
Full-time
Apache AirflowApache SparkPythonAWSApache KafkaSQLFastAPINode.jsTerraformBig DataBigQueryVector DatabasesLLMLangChainMongoDBPySparkGCPMySQLDatabricksDockerMicrosoft AzureKubernetesData WarehouseETLPyTorchOpenAI APISnowflakeDBTDagsterDatadogClaude Code
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Joshua
AI Engineer
United States/(GMT -5:00)
Joshua is a Senior Backend and AI Engineer with a strong focus on system architecture, applied AI, and real-world product delivery. He brings deep experience in building and scaling robust backend systems, with particular strength in designing LLM-powered applications using vector databases, RAG pipelines, and modern infrastructure patterns. Experienced in tools like LangChain, Bedrock, and OpenAPI, Joshua is comfortable navigating CI/CD, Kubernetes, and AWS and GCP environments. He has held lead roles across multiple teams and has experience as a public speaker at industry conferences.
Experience
20 years
Availability
Part-time & Full-time
Apache AirflowPythonAWSLLMNext.jsNest.jsOpenAIKubernetesLangChainGCPFirebaseDjango RESTTerraformTerragruntReact NativeReactAmazon ECSAmazon S3Amazon SQSAWS LambdaLangGraphSnowflakePostgreSQLPinecone
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Jakub
Data Engineer
Poland/(GMT +1:00)
Jakub is an experienced Data Engineer with a solid educational foundation in computer science and a comprehensive grasp of the AWS platform. Proficient in SQL and adept at navigating complex data tasks with ease. This candidate is able to demonstrate strength in project management, complemented by diverse domain experience spanning fintech, marketing, and beyond.
Experience
6 years
Availability
Part-time & Full-time
Apache AirflowPythonSnowflakeSQLAWSDockerData WarehouseBusiness intelligenceData ModelingApache SparkGPTETLData analysisTableauOpenAICI/CDTerraformAIData visualizationPySparkMicrosoft SQL ServerLookerPowerBIDatabricksPostgreSQLGCPPL/SQLElasticSearchBigQueryReactAirflowDagsterPandasDBTSQLAlchemyClaude CodeCursorREST APIDatabase design
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Mario
Data Engineer
Guatemala/(GMT -6:00)
Mario is a versatile Senior Data Engineer with 17 years of experience, including leadership roles as Team Lead and CTO. He demonstrates strong self-presentation, business-oriented thinking, and proven leadership. Technical interviews confirm proficiency in SQL and architectural decision-making, with additional strengths in communication and project delivery. He is fluent in English and comfortable in both solo and team settings.
Experience
19 years
Availability
Full-time
Apache AirflowPythonAWSETLSQLMicrosoft SQL ServerData WarehouseApache HadoopKubernetesDockerSnowflakeAzure DevOpsRedshiftAPIAmazon RDSAmazon EC2PostgreSQLAWS LambdaAmazon S3PandasMySQLMongoDBDBTDatabricks
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Abdulhakeem
Data Engineer
Germany/(GMT +1:00)
Abdulhakeem has a lifelong passion for numbers and discovering insightful patterns within them. Although he started as a Data Engineer, Abdulhakeem has now grown into a senior AI engineer who can bridge data systems and LLM integration. He's adept at tackling projects of any complexity and would excel in roles ranging from engineering to solution architecture and team leading. Additionally, he is well-versed in cloud solutions like AWS and GCP. Despite his extensive experience in tech, Abdulhakeem also finds joy in music and has recorded his own tracks in the past.
Experience
12 years
Availability
Part-time & Full-time
Apache AirflowSQLPythonMachine learningAWSLLMOpenAIAITensorflowData ScienceTableauNumPyPandasApache SparkGCPGraphQLBigQuerySnowflakeDBTLookerDatabricksData WarehousePostgreSQLDockerTerraformLangChainPyTorchMongoDBETLNLPMLOpsVector DatabasesFirebasePowerBIClaude LLMKubernetes
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Tarik
AI Engineer
Turkey/(GMT +3:00)
Tarik is an expert in data engineering, machine learning, and large language models, holding a PhD in Graph Theory. He is also proficient in Python, Pandas, and Scikit-learn, and has successfully designed and delivered advanced NLP and AI solutions, including cutting-edge RAG systems. With a deep understanding of machine learning concepts and exceptional problem-solving skills, Tarik consistently drives impactful results in AI-driven and data-intensive projects!
Experience
12 years
Availability
Part-time & Full-time
Apache AirflowPythonAWSLLMDockerFastAPINLPPandasScikit-learnAI agent developmentBigQueryElasticSearchApache KafkaAIPostgreSQLUbuntuAPIDeep LearningRedisOpenAI APIFirebaseMicrosoft AzureSnowflakeGCPMLOpsLangChainPyTorchNeural NetworksTensorflowOAuthKafkaAirflowFlask
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Bruno
Data Engineer
Brazil/(GMT -3:00)
Bruno is an experienced Senior Data Engineer with over 6 years of expertise in creating ETL/ELT Data Pipelines. He is skilled in Python, SQL, and other data technologies. Bruno has an impressive record of leading the entire Big Data Team and has played a crucial role in raising the delivery standards. In addition to his technical skills, Bruno shares his insights on his YouTube channel. With Bruno's expertise, you can expect top-notch data engineering and team leadership.
Experience
9 years
Availability
Part-time & Full-time
Apache AirflowPythonTableauAWSApache SparkPostgreSQLClickHouseSnowflakeApollo GraphQLKafkaLiquibaseSQLScrapyPySparkGCPBigQueryBitbucketAirflowAmazon EC2Data Warehouse
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Nialish
Data Engineer
Germany/(GMT +1:00)
Nialish is a Senior Data Engineer with over six years of experience in data engineering, big data analytics, and the development of scalable, distributed data pipelines. He possesses hands-on expertise in Python, Apache Airflow, Snowflake, SQL, and ETL/Data Warehouse architecture, among other related technologies. Throughout his career, Nialish has worked across various industries, delivering reliable, secure, and high-performance data solutions in fast-paced environments. He excels at designing and debugging complex data pipelines while effectively balancing technical best practices with business requirements to provide actionable insights. He demonstrates strong problem-solving, critical thinking, and business acumen, with the ability to communicate and collaborate across technical and non-technical teams. Adaptable, eager to learn, and experienced in leading teams, he thrives in agile environments and embraces new challenges.
Experience
10 years
Availability
Full-time
Apache AirflowMicrosoft AzurePythonSQLAWSLangChainCI/CDLLMAIDockerKubernetesPySparkDatabricksMachine learningApache KafkaData ModelingSnowflakeRedisDeep LearningBig DataMongoDBAzure SQLApache SparkDBT
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Guilhermo
Data Engineer
Brazil/(GMT -3:00)
Guilhermo is an experienced Senior Data Engineer with around 8 years of hands-on experience in the field. He possesses extensive knowledge of SQL, Python, Azure, and AWS, which makes him an expert in his field. Guilhermo also has a valuable background in handling AI-driven data workflows and model deployment infrastructure. His understanding of debugging concepts and ability to make sound architectural choices enables him to create robust data solutions. He has gained valuable experience with startups and is familiar with the dynamic nature of the industry. Guilhermo's proactive approach and evident enthusiasm for his work make him an excellent addition to any team.
Experience
11 years
Availability
Full-time
Apache AirflowMicrosoft AzurePythonAWSDatabricksSQLGCPDevOpsApache HadoopMicrosoft Power BIBigQueryETLApacheRedshiftBig DataSnowflakeData analysisApache SparkMySQLData WarehouseLookerTableauAWS SageMakerAirflowKafkaData ModelingSeleniumBeautifulSoup
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What you should know about modern Apache Airflow devs

by Lana Bochek

Recruiter at Lemon.io

Verified Expert

Hiring Guide: Apache Airflow Developers

Why Hire Apache Airflow Developers?

Apache Airflow developers are data engineering experts who specialize in building, orchestrating, and managing complex workflows and data pipelines. As data-driven operations become critical for every industry, Airflow has emerged as the go-to tool for workflow automation and pipeline scheduling. Hiring skilled Apache Airflow developers ensures your business can automate ETL processes, integrate multiple data sources, and manage dependencies with precision.

Airflow developers help companies scale analytics infrastructure, improve data reliability, and maintain observability across workflows. Whether your data environment runs on AWS, GCP, or Azure, an experienced Airflow developer can seamlessly integrate Airflow into your ecosystem for continuous and automated data movement.

Search Intent & Keywords

Popular searches leading to this page include: hire Apache Airflow developers, Airflow DAG developer, Airflow data pipeline expert, ETL workflow automation engineer, Airflow consultant, Airflow on AWS/GCP developer, and Airflow orchestration developer. Long-tail keywords include: custom Airflow DAG creation, Airflow developer freelance, Airflow data engineering services, and hire Airflow consultant for ETL pipelines.

Core Responsibilities of Apache Airflow Developers

Design, implement, and maintain data pipelines using Apache Airflow DAGs (Directed Acyclic Graphs).

Develop, test, and schedule workflows for ETL, ELT, and data synchronization across multiple systems.

Integrate Airflow with data warehouses, APIs, and cloud services such as AWS S3, GCP BigQuery, Azure Data Lake, or Snowflake.

Implement monitoring and alerting mechanisms using Airflow’s metadata database and third-party observability tools.

Automate data ingestion, transformation, and quality assurance pipelines with scalable, reusable components.

Collaborate with data scientists, analysts, and DevOps teams to streamline data flow and ensure high availability.

Optimize Airflow performance through DAG parallelization, task retries, and resource management.

Essential Technical Skills

Programming Languages: Python (Airflow’s primary language), SQL, Bash scripting.

Data Pipelines: ETL/ELT design, orchestration, data modeling, dependency management.

Cloud Integration: AWS (Lambda, Redshift, S3), GCP (BigQuery, Dataflow), Azure (Data Factory, Blob Storage).

Databases: PostgreSQL, MySQL, MongoDB, and Snowflake.

Airflow Components: DAGs, Operators, Sensors, XComs, TaskFlow API, Airflow Scheduler, and Worker management.

Containerization & CI/CD: Docker, Kubernetes, Jenkins, GitHub Actions for Airflow deployment and scaling.

Monitoring: Prometheus, Grafana, and Airflow’s built-in logging and alerting mechanisms.

Interview Framework for Airflow Developers

Airflow Fundamentals (15 min): Ask the candidate to explain the Airflow architecture, components, and scheduler behavior.

DAG Design (25 min): Evaluate their ability to design a complex DAG with dependencies, retries, and conditional branching.

Cloud Integration (15 min): Discuss how they connect Airflow with cloud-based storage, compute, or data warehouses.

Troubleshooting (10 min): Assess how they debug failed DAGs, handle task timeouts, or manage Airflow cluster performance.

Optimization & Security (10 min): Review how they use variables, secrets, and Airflow pools to improve efficiency and protect sensitive data.

Budget & Hiring Expectations

Airflow developers are typically mid- to senior-level data engineers with deep knowledge of workflow automation. Rates vary depending on data ecosystem complexity and required cloud integration expertise:

Mid-level developers (2–4 years): $60–$90/hour — build standard ETL workflows and manage Airflow deployment in cloud environments.

Senior developers (5+ years): $100–$150/hour — specialize in optimizing complex DAGs, handling scalability, and managing Airflow clusters.

Consultants & Architects: $160–$220/hour — design large-scale data infrastructure and manage hybrid data orchestration environments.

14-Day Onboarding Roadmap

Set up Airflow environment locally or in the cloud (Docker, MWAA, or Composer).

Audit existing pipelines and define priority workflows for automation.

Implement data extraction, transformation, and load DAGs.

Integrate Airflow with third-party data tools (Kafka, DBT, or Snowflake).

Set up Airflow monitoring, alerts, and documentation for maintainability.

Red Flags When Hiring Airflow Developers

Cannot explain DAG scheduling, retries, or XComs clearly.

No experience with Airflow plugins, operators, or hooks.

Lack of cloud-based Airflow deployment experience (e.g., AWS MWAA or GCP Composer).

Does not implement monitoring or fails to manage Airflow metadata efficiently.

Overcomplicates workflows without modular or reusable structures.

Key Interview Questions

“What’s the difference between Airflow Sensors and Operators?”

“How do you handle dependencies and retries in Airflow DAGs?”

“Describe how Airflow’s scheduler and executor work together.”

“What’s your approach to monitoring Airflow DAG performance?”

“How do you handle Airflow authentication and secrets management?”

Data Engineer Job Description – for teams needing Airflow developers who manage complex data pipelines.

Python Developer Job Description – since Airflow’s core development relies heavily on Python.

DevOps Engineer Job Description – for managing Airflow deployment, containerization, and CI/CD workflows.

Cloud Engineer Job Description – for integrating Airflow into AWS, GCP, or Azure ecosystems.

FAQ: Hiring Apache Airflow Developers

What is Apache Airflow used for?

Apache Airflow is an open-source platform for orchestrating workflows and automating data pipelines. It helps manage ETL, machine learning, and reporting tasks efficiently.

Do I need a dedicated Airflow developer?

If your business handles large data sets or relies on automated workflows, a dedicated Airflow developer ensures scalability, performance, and maintainability of your data systems.

Is Airflow suitable for real-time data processing?

Airflow is best for batch workflows but can integrate with real-time systems like Apache Kafka or Spark Streaming for near-real-time data orchestration.

How does Airflow integrate with cloud platforms?

Airflow integrates seamlessly with AWS, GCP, and Azure using their managed Airflow services (MWAA, Composer, ADF) and cloud-native operators.

Ready to Hire Apache Airflow Developers?

Get matched with top Apache Airflow developers in under 48 hours. Lemon.io connects you with vetted data engineering professionals who can build, optimize, and scale your Airflow workflows for seamless automation and data reliability.

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Why hire Apache Airflow

Over 6500 companies use Apache Airflow as their main framework, including giants like Google, Amazon and Lyft.

High-quality web apps

Apache Airflow's modular approach lets devs reuse components, so they can seamlessly manage app complexity.

Faster development process

Apache Airflow's architecture and a vast library of pre-built components speed up development, so you can get to market sooner.

Enjoyable user experience

From smooth, responsive interfaces to reduced loading times, devs can use Apache Airflow to boost usability.

Scaling made easy

Apache Airflow's modular, component-based architecture makes it easy to scale applications as traffic grows.

Case studies

Aerospace

The experience with Lemon.io has been fantastic. The interview process has been good, the caliber of people – excellent and integration has been very smooth.

Marc HorowitzCOO of SkyFi

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Telecommunications

We needed extra developers to clean off all these bugs so the company could skyrocket.

Conor MackenDirector of Engineering

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We needed extra AI engineers to keep our ambitious project running.

Mike LukimanFounding Senior Software Engineer at Everstar.ai

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Frequently asked questions

Apache Airflow developers are typically found within the data engineering and workflow automation communities. To start your search, look at general job boards like Glassdoor, LinkedIn, and Indeed using keywords such as “Apache Airflow,” “data engineering,” and “workflow automation.”

Additionally, explore specialized platforms and communities related to data engineering, such as forums and conferences focused on big data, data science, and ETL (Extract, Transform, Load) processes.

For a streamlined approach, consider using targeted recruiting platforms like Lemon.io. We can connect you with experienced Apache Airflow developers quickly, often within 48 hours or less. Our pre-vetted candidates have undergone thorough screening to ensure they meet your specific needs in data engineering and workflow automation.

At Lemon.io, when you choose one of our Apache Airflow developers, we want you to have a worry-free hiring experience. Therefore, we offer a paid no-risk trial of up to 20 hours, allowing you to see firsthand how the developer tackles your project’s specific needs using Apache Airflow.

You can rest easy knowing that our no-risk replacement guarantee is in place. A quick and seamless transition to another candidate is guaranteed if the developer doesn’t meet your requirements. While replacements are rarely needed due to our rigorous vetting process, we provide this option to ensure your complete satisfaction.

Yes, the demand for Apache Airflow developers is growing as more companies see the benefits of automating and managing complex data tasks.

Apache Airflow is a key tool for organizing and controlling data workflows, making it essential in industries like finance, healthcare, and tech. Companies are looking for developers who know how to build and maintain these workflows to keep their data operations running smoothly.

You need a skilled Apache Airflow developer to optimize your workflow automation, and at Lemon.io, we make that happen swiftly. We provide a curated shortlist of vetted Apache Airflow experts within 48 hours, connecting you with top talent experienced in managing complex workflows and data pipelines. Most of our clients successfully integrate their new Apache Airflow developer quickly, ensuring minimal disruption to their operations.

Our network spans over 300 different websites and online programmer communities that we constantly scan to find the most sought-after Apache Airflow experts.

Every engineer we introduce to you is carefully assessed through a multi-stage evaluation process; only 1% of those applying actually join us at Lemon.io.

Lemon.io guarantees a fast and efficient substitution if any technical or project hurdles arise during a developer’s engagement. However, rest assured, this is a rare occurrence—replacement is an option, not the norm for us.

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