Hire Apache Airflow Developers to Orchestrate Your Data Workflows

Apache Airflow

Seamless data pipeline automation with Upstaff’s expert Apache Airflow developers: Whether you need to hire an Apache Airflow developer for scalable ETL processes, Data Science pipelines, or cloud-based orchestration, our pre-vetted Apache Airflow engineers deliver tailored solutions to streamline your operations. Find top talent proficient in Apache Airflow, Python, and cloud integrations to drive efficiency and innovation.

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Apache Airflow

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Azure 5yr.
Python 4yr.
SQL 5yr.
Cloudera 2yr.
Apache Spark
JSON
PySpark
XML
Apache Airflow
AWS Athena
Databricks
Data modeling Kimbal
Microsoft Azure Synapse Analytics
Power BI
Tableau
AWS ElasticSearch
AWS Redshift
dbt
HDFS
Microsoft Azure SQL Server
NoSQL
Oracle Database
Snowflake
Spark SQL
SSAS
SSIS
SSRS
AWS
GCP
AWS EMR
AWS Glue
AWS Glue Studio
AWS S3
Azure HDInsight
Azure Key Vault
API
Grafana
Inmon
REST
Kafka
databases
...

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

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Seniority Senior (5-10 years)
Location Norway
Python 6yr.
SQL 6yr.
JMeter 6yr.
PySpark 6yr.
Apache Airflow 5yr.
AWS ML (Amazon Machine learning services)
Apex DataLoader
Spring Data
Apache Spark Streaming
Data Analysis
Data Quality
Data visualization
ELT
Lakehouse
Data Warehousing
AWS Kinesis
AWS Security Groups
Microsoft Azure API
3D Modelling
CI/CD
Code Review
Cloud solutions
Data backups
Illustration and icons
Privacy Compliance & Data Governance
...

A seasoned Data Engineer with over 6 years of experience in the field of software and big data engineering. Holds a strong academic background in Computer Science and Software Engineering, certified as a Google Cloud Professional Data Engineer. Demonstrates deep expertise in high-load system design, performance optimizations, and domain-specific solutions for Healthcare, Fintech, and E-commerce. Proficient in Python and SQL, with significant exposure to data engineering tools such as Apache Hadoop, Apache Spark, and Apache Airflow, and cloud technologies from AWS and GCP. Adept at working with various databases and message brokers, excelling in data modeling, BI, and data visualization using tools like Looker, Power BI, and Tableau. Enhanced system efficiencies through SQL and data pipeline optimizations, driving significant improvements in processing speed and system performance. A collaborative engineer with a strong grasp of DevOps practices, committed to best-in-class data governance and security standards.

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Seniority Senior (5-10 years)
Location Poland
SQL
ETL
Power BI
DAX Studio
Git
Python
PySpark
Apache Airflow
Business Analysis
Data Analysis
Data Analysis Expressions (DAX)
Tableau
Spark SQL
Cloud Functions
Google Data Studio
Docker
Excel
FDD
UML
Usability tests
Data Structures
Mathematics
Unreal Engine
...

- 3+ years of experience as a BI Engineer; - Strong abilities in Power BI, SSIS, Tableau, and Google Data Studio; - Deep skills in developing and optimizing ETL processes within business intelligence; - Experience with SQL, Python; - Familiar with Docker, Apache Airflow, and PySpark; - Good knowledge of data warehousing and business intelligence principles.

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Seniority Middle (3-5 years)
Location Czech Republic
Python
PySpark
Docker
Apache Airflow
Kubernetes
NumPy
Scikit-learn
TensorFlow
Scala
C/C++/C#
Crashlytics
Pandas
Airbyte
Apache Hive
AWS Athena
Databricks
Apache Druid
AWS EMR
AWS Glue
API
Stripe
Delta lake
DMS
Xano
...

- 4+ years of experience as a Data Engineer, focused on ETL automation, data pipeline development, and optimization; - Strong skills in SQL, DBT, Airflow (Python), and experience with SAS, PostgreSQL, and BigQuery for building and optimizing ETL processes; - Experience working with Google Cloud (GCP) and AWS: utilizing GCP Storage, Pub/Sub, BigQuery, AWS S3, Glue, and Lambda for data processing and storage; - Built and automated ETL processes using DBT Cloud, integrated external APIs, and managed microservice deployments; - Optimized SDKs for data collection and transmission through Google Cloud Pub/Sub, used MongoDB for storing unstructured data; - Designed data pipelines for e-commerce: orchestrated complex processes with Druid, MinIO, Superset, and AWS for data analytics and processing; - Worked with big data and stream processing: using Apache Spark, Kafka, and Databricks for efficient transformation and analysis; - Amazon sales forecasting using ClickHouse, Vertex AI, integrated analytical models into business processes; - Experience in Data Lake migration and optimization of data storage, deploying cloud infrastructure and serverless solutions on AWS Lambda, Glue, and S3.

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Seniority Middle (3-5 years)
Python 7yr.
SQL 7yr.
JMeter 7yr.
PySpark 7yr.
Apache Airflow 4yr.
NumPy 7yr.
Pandas 7yr.
Matplotlib 6yr.
Scikit-learn 3yr.
MongoDB 3yr.
PostgreSQL 3yr.
Redis 3yr.
GCP Cloudbuild 3yr.
DB MS SQL 3yr.
PyTorch 2yr.
TensorFlow 2yr.
Plotly 2yr.
Neo4j 2yr.
Oracle Database 2yr.
Azure 2yr.
Kubernetes 2yr.
Hugging Face 1yr.
SciPy 1yr.
Looker Studio
...

Data Engineer with 7 years of expertise in data analytics/science, ETL, and cloud technologies, blending deep healthcare and pharma industry knowledge. Proficient in Python, SQL, and a suite of data engineering tools including, Apache Spark, Airflow, and BI tools such as Power BI. Implemented real-time data streaming using Kafka, and has experience with multiple cloud services from AWS, Azure, and GCP. Key achievements include optimizing SQL database performance, automating data quality checks, and uncovering new drug candidates through computational data discovery—demonstrating a strong fusion of domain knowledge and technical acumen.

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Seniority Senior (5-10 years)
Location Poland
AWS big data services 5yr.
Microsoft Azure 3yr.
Python
ETL
AWS ML (Amazon Machine learning services)
Keras
Machine Learning
OpenCV
TensorFlow
Theano
C#
C++
Scala
Apache Spark
Apache Spark 2
Big Data Fundamentals via PySpark
Deep Learning in Python
Linear Classifiers in Python
Pandas
PySpark
.NET
.NET Core
.NET Framework
Apache Airflow
Apache Hive
Apache Oozie 4
Data Analysis
Superset
Apache Hadoop
AWS Database
dbt
HDP
Microsoft SQL Server
pgSQL
PostgreSQL
Snowflake
SQL
AWS
GCP
AWS Quicksight
AWS Storage
GCP AI
GCP Big Data services
Kafka
Kubernetes
OpenZeppelin
Qt Framework
YARN 3
SPLL
...

- Data Engineer with a Ph.D. degree in Measurement methods, Master of industrial automation - 16+ years experience with data-driven projects - Strong background in statistics, machine learning, AI, and predictive modeling of big data sets. - AWS Certified Data Analytics. AWS Certified Cloud Practitioner. Microsoft Azure services. - Experience in ETL operations and data curation - PostgreSQL, SQL, Microsoft SQL, MySQL, Snowflake - Big Data Fundamentals via PySpark, Google Cloud, AWS. - Python, Scala, C#, C++ - Skills and knowledge to design and build analytics reports, from data preparation to visualization in BI systems.

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Seniority Expert (10+ years)
Location Ukraine
Python 10yr.
SQL 10yr.
Talend ETL 10yr.
AWS EC2
Apache Airflow
PHP
Apex DataLoader
Salesforce
C/C++/C#
Jasperreports
Schedulers
Pandas
PySpark
AWS Athena
Jupyter Notebook
Presto S3
Teradata
AWS ElasticSearch
AWS Redshift
Microsoft SQL Server
MySQL
Oracle Database
PostgreSQL
SQLAlchemy
AWS Boto3
AWS Cloudformation
AWS EMR
AWS Lambda
AWS S3
Google Data Studio
Datadog
JMeter
New Relic
Cloud IAM
MWAA
RDS
RMAN
Unix\Linux
...

- Senior Data Engineer with 10+ of experience specializing in designing, optimizing, and maintaining data infrastructures, data flow automation, and algorithm development. - Has expertise in Python, SQL/NoSQL, ETL processes, PySpark, Apache Airflow, and an array of AWS services, complemented by a strong foundation in database systems and cloud-based solutions. Proven capability in handling large-scale data analytics and processing with a focus on performance and cost efficiency in cloud environments. Proficient in developing robust ETL pipelines, performing data migrations, and optimizing complex queries and storage procedures, leveraging extensive experience across multiple industries and platforms. - Start: ASAP - English: Upper-Intermediate

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Seniority Expert (10+ years)
Location Limassol, Spain
Scala
NLP
Akka
Apache Spark
Akka Actors
Akka Streams
Cluster
Scala SBT
Scalatest
Apache Airflow
Apache Hadoop
AWS ElasticSearch
PostgreSQL
Slick database query
AWS
GCP
Haddop
Microsoft Azure API
ArgoCD
CI/CD
GitLab CI
Helm
Travis CI
GitLab
HTTP
Kerberos
Kafka
RabbitMQ
Keycloak
Swagger
Kubernetes
Terraform
Observer
Responsive Design
Unreal Engine
...

Software Engineer with proficiency in data engineering, specializing in backend development and data processing. Accrued expertise in building and maintaining scalable data systems using technologies such as Scala, Akka, SBT, ScalaTest, Elasticsearch, RabbitMQ, Kubernetes, and cloud platforms like AWS and Google Cloud. Holds a solid foundation in computer science with a Master's degree in Software Engineering, ongoing Ph.D. studies, and advanced certifications. Demonstrates strong proficiency in English, underpinned by international experience. Adept at incorporating CI/CD practices, contributing to all stages of the software development lifecycle. Track record of enhancing querying capabilities through native language text processing and executing complex CI/CD pipelines. Distinguished by technical agility, consistently delivering improvements in processing flows and back-end systems.

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Seniority Senior (5-10 years)
Location Ukraine

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Table of Contents

TOP 10 Apache Airflow Related Technologies

  • Python

    Python is the most popular programming language for Apache Airflow development. Its simplicity, readability, and extensive library support make it a top choice for developers. With Python, you can easily create and manage workflows, handle data processing tasks, and integrate with various systems.

  • Apache Airflow

    Apache Airflow itself is a critical technology for software development. It is an open-source platform that allows you to programmatically schedule, monitor, and manage workflows. With its powerful task orchestration capabilities and rich UI, Apache Airflow greatly simplifies the development and deployment of data pipelines.

  • SQLAlchemy

    SQLAlchemy is a popular SQL toolkit and Object-Relational Mapping (ORM) library in the Python ecosystem. It provides a convenient way to interact with databases and execute SQL queries. Apache Airflow leverages SQLAlchemy for defining and managing connections to various database systems.

  • Docker

    Docker is a containerization platform widely used in software development. It allows you to package your application and its dependencies into a lightweight, portable container. Apache Airflow can be easily deployed and scaled using Docker containers, enabling efficient resource utilization and easier deployment across different environments.

  • Kubernetes

    Kubernetes is a container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides a reliable and scalable infrastructure for running Apache Airflow in a production environment. With Kubernetes, you can easily manage the lifecycle of Airflow deployments and ensure high availability.

  • Git

    Git is the most widely used version control system in software development. It allows multiple developers to collaborate on a project, track changes, and manage code branches. Apache Airflow projects benefit from using Git for version control, enabling efficient collaboration and easy rollback to previous versions if needed.

  • Amazon Web Services (AWS)

    AWS is a leading cloud computing platform that offers a wide range of services for building and deploying applications. Apache Airflow can be easily integrated with AWS services such as Amazon S3, Amazon Redshift, and AWS Lambda, enabling seamless data processing and workflow automation in the cloud.

Hard skills of a Apache Airflow Developer

Apache Airflow is an open-source platform used for orchestrating and scheduling complex data pipelines. As an Apache Airflow Developer, having the right hard skills is crucial to effectively design, develop, and maintain these pipelines. Here are the hard skills required for different levels of expertise:

Junior

  • Python: Proficiency in Python programming language to write and maintain code for Apache Airflow workflows.
  • Apache Airflow: Understanding of the core concepts and components of Apache Airflow, including DAGs, Operators, and Executors.
  • SQL: Basic knowledge of SQL to interact with databases and perform data transformations within the pipelines.
  • Git: Familiarity with version control systems like Git to manage code repositories and collaborate with other developers.
  • Debugging and Troubleshooting: Ability to identify and resolve issues in Apache Airflow workflows through debugging and troubleshooting techniques.

Middle

  • Data Modeling: Proficiency in designing and implementing data models to represent complex business logic within Apache Airflow workflows.
  • ETL: Experience in Extract, Transform, Load (ETL) processes and tools, including data ingestion, cleansing, and transformation.
  • Cloud Platforms: Knowledge of cloud platforms like AWS, GCP, or Azure to deploy and manage Apache Airflow on cloud infrastructure.
  • Database Systems: Understanding of different database systems such as MySQL, PostgreSQL, or Oracle, and their integration with Apache Airflow.
  • Monitoring and Alerting: Familiarity with monitoring and alerting tools to ensure the smooth functioning of Apache Airflow workflows.
  • Performance Optimization: Ability to identify and optimize performance bottlenecks in Apache Airflow workflows for efficient execution.
  • Containerization: Knowledge of containerization technologies like Docker and container orchestration platforms like Kubernetes.

Senior

  • Advanced Python: In-depth knowledge of Python programming language, including advanced concepts like generators, decorators, and metaclasses.
  • Scaling and High Availability: Experience in scaling Apache Airflow to handle large-scale data pipelines and ensuring high availability.
  • Security and Authentication: Understanding of security best practices and implementing authentication mechanisms to secure Apache Airflow.
  • Data Warehousing: Proficiency in data warehousing concepts and tools like Snowflake, Redshift, or BigQuery for efficient data storage and retrieval.
  • Performance Tuning: Expertise in fine-tuning Apache Airflow configurations and optimizing resource utilization for improved performance.
  • CI/CD: Experience in setting up continuous integration and deployment pipelines for Apache Airflow workflows using tools like Jenkins or GitLab.
  • Documentation and Code Review: Ability to write comprehensive documentation and perform code reviews to ensure high-quality codebase.
  • Team Leadership: Strong leadership skills to mentor junior developers, coordinate with cross-functional teams, and drive project success.

Expert/Team Lead

  • Big Data Technologies: Proficiency in working with big data technologies like Hadoop, Spark, or Kafka for processing and analyzing large volumes of data.
  • Advanced SQL: Deep understanding of SQL and query optimization techniques for complex data transformations and analysis.
  • Machine Learning: Knowledge of machine learning concepts and frameworks like TensorFlow or PyTorch for integrating machine learning models into Apache Airflow pipelines.
  • DevOps: Experience in DevOps practices and tools like Ansible, Terraform, or Helm for automating infrastructure provisioning and deployment.
  • Architecture Design: Ability to design scalable and robust architecture for Apache Airflow workflows, considering factors like fault tolerance and data consistency.
  • Performance Monitoring: Proficiency in monitoring and analyzing performance metrics of Apache Airflow workflows using tools like Prometheus or Grafana.
  • Data Governance: Understanding of data governance principles and implementing data lineage, quality checks, and access controls within Apache Airflow.
  • Business Intelligence: Familiarity with business intelligence tools like Tableau or Power BI for visualizing and reporting data processed by Apache Airflow.
  • Presentation and Communication: Excellent presentation and communication skills to effectively convey complex technical concepts to stakeholders and clients.
  • Agile Methodologies: Experience in working in Agile development environments, adhering to Agile principles and practices for efficient project management.
  • Problem Solving: Strong problem-solving skills to analyze and resolve complex issues in Apache Airflow workflows, ensuring smooth data pipeline execution.

What are top Apache Airflow instruments and tools?

  • Apache Airflow: Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It was initially developed by Airbnb in 2014 and later became an Apache Software Foundation project in 2016. Airflow allows users to define workflows as directed acyclic graphs (DAGs) and provides a rich set of operators to execute tasks. It has gained popularity for its ability to handle complex data processing and orchestration tasks efficiently.
  • Astronomer: Astronomer is a platform that provides a managed Apache Airflow service. It simplifies the deployment and management of Airflow infrastructure, allowing users to focus on building data pipelines rather than dealing with infrastructure setup. Astronomer offers features such as scalability, monitoring, and security enhancements, making it an excellent choice for organizations that want a hassle-free Airflow experience.
  • Superset: Superset is a data exploration and visualization platform that integrates well with Apache Airflow. It allows users to create interactive dashboards and perform ad-hoc analysis on data generated by Airflow workflows. Superset supports various data sources and provides a user-friendly interface for data exploration, making it a powerful tool for data-driven organizations.
  • Puckel/Docker-Airflow: Docker-Airflow is a Docker image maintained by Puckel that provides a pre-configured environment for running Apache Airflow. It simplifies the setup process by packaging Airflow and its dependencies into a single container. Docker-Airflow is widely used in the Airflow community as it offers an easy way to get started with Airflow and ensures consistency across different environments.
  • Apache Kafka: Apache Kafka is a distributed streaming platform that can be seamlessly integrated with Apache Airflow. Kafka provides a highly scalable and fault-tolerant messaging system, which makes it an ideal choice for handling real-time data streams. By connecting Airflow with Kafka, users can build robust data pipelines that can process and react to streaming data in near real-time.
  • Google Cloud Composer: Google Cloud Composer is a fully managed workflow orchestration service based on Apache Airflow. It offers a serverless environment for running Airflow workflows on Google Cloud Platform (GCP). Cloud Composer provides features like automatic scaling, monitoring, and integration with other GCP services, enabling users to build and deploy scalable data pipelines effortlessly.
  • Apache Spark: Apache Spark is a powerful distributed computing framework that can be integrated with Apache Airflow. Spark enables high-speed data processing and supports various data formats, making it suitable for big data analytics. By combining the capabilities of Airflow and Spark, users can build end-to-end data pipelines that involve data ingestion, transformation, and analysis.

How and where is Apache Airflow used?

Case NameCase Description
Data Pipeline OrchestrationApache Airflow is widely used for orchestrating complex data pipelines. It allows users to define, schedule, and monitor workflows that involve multiple tasks such as data extraction, transformation, and loading (ETL). With its intuitive interface and powerful task management capabilities, Airflow makes it easy to build and manage scalable data processing pipelines. For example, a company may use Airflow to schedule and coordinate the extraction of data from various sources, perform transformations on the data, and load it into a data warehouse for further analysis.
Machine Learning Model Training and DeploymentAirflow provides a reliable framework for managing the end-to-end process of training and deploying machine learning models. It enables data scientists to schedule and automate the execution of model training tasks, ensuring that models are trained on the latest data and deployed in a timely manner. Airflow’s extensible architecture also allows for seamless integration with popular machine learning frameworks such as TensorFlow and PyTorch. For instance, a data science team can leverage Airflow to schedule regular model training jobs, perform hyperparameter tuning, and deploy the trained models to production environments.
Real-time Data ProcessingWith its ability to handle both batch and streaming data, Airflow is a valuable tool for real-time data processing. It supports integrations with streaming platforms like Apache Kafka and Apache Pulsar, enabling the creation of dynamic data pipelines that can process incoming data in real-time. Organizations can utilize Airflow to build robust streaming data workflows for applications such as real-time analytics, fraud detection, and IoT data processing.
Workflow Monitoring and AlertingAirflow offers a comprehensive monitoring and alerting system that allows users to track the progress and health of their workflows. It provides a rich set of built-in monitoring features, including task status tracking, task duration metrics, and task retries. Additionally, Airflow supports integration with popular monitoring tools like Prometheus and Grafana, enabling users to visualize and analyze workflow metrics in real-time. This ensures that any issues or bottlenecks in the workflows can be quickly identified and addressed.
Event-driven Data PipelinesAirflow’s event-driven architecture makes it a suitable choice for building data pipelines that are triggered by external events. It can seamlessly integrate with event-driven systems like Apache Kafka or Amazon Simple Notification Service (SNS), allowing workflows to be triggered based on specific events or conditions. This capability is particularly useful in scenarios where data processing needs to be triggered in response to real-time events, such as processing incoming data from IoT devices or reacting to user interactions in web applications.

TOP 15 Tech facts and history of creation and versions about Apache Airflow Development

  • Apache Airflow is an open-source workflow management platform developed by Airbnb in 2014.
  • It was created by Maxime Beauchemin, a data engineer at Airbnb, who wanted to solve the challenges of managing complex data workflows.
  • Airflow uses a Directed Acyclic Graph (DAG) methodology, allowing users to define, schedule, and monitor their workflows as code.
  • One of the groundbreaking features of Airflow is its ability to handle dependency management and task scheduling, ensuring that tasks are executed in the correct order.
  • With Airflow, developers can easily build, schedule, and monitor workflows that involve multiple tasks and dependencies.
  • It provides a web-based UI that allows users to visualize and monitor the progress of their workflows.
  • Airflow supports various data processing frameworks, including Hadoop, Spark, and Hive, making it a versatile tool for data engineering and data science tasks.
  • It has a vibrant and active community, with contributions from many organizations and individuals.
  • Airflow has become one of the most popular workflow management platforms in the industry, with a large user base and widespread adoption.
  • Many well-known companies, such as Airbnb, Lyft, and Twitter, rely on Airflow for their data workflow needs.
  • Apache Airflow has a rich ecosystem of plugins and integrations, allowing users to extend its functionality and integrate with other tools and services.
  • It has a comprehensive documentation and a strong focus on code quality and maintainability.
  • Airflow has a release cycle, with regular updates and bug fixes, ensuring that users have access to the latest features and improvements.
  • The latest stable version of Apache Airflow is 2.1.2, released on August 31, 2021.
  • Airflow has a strong commitment to backward compatibility, making it easier for users to upgrade to newer versions without breaking their existing workflows.
Table of Contents

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FAQs for Hiring Apache Airflow Developers

What is Apache Airflow? Arrow

Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor complex data workflows. It allows Apache Airflow developers to define tasks and dependencies as code using Python, making it ideal for orchestrating ETL pipelines, machine learning workflows, and data processing tasks. At Upstaff, our Airflow experts leverage this tool to build scalable, automated solutions for your data needs.

What is Apache Airflow used for? Arrow

Apache Airflow is used for orchestrating data pipelines, automating tasks, and managing dependencies in data engineering projects. It’s widely applied in Data Science, ETL processes, and cloud-based data orchestration for industries like finance, e-commerce, and tech. Apache Airflow developers for hire at Upstaff use it to streamline workflows, integrate with tools like AWS, Google Cloud, and Snowflake, and ensure reliable data processing.

Is Apache Airflow free? Arrow

Yes, Apache Airflow is a free, open-source platform maintained by the Apache Software Foundation. While the core software incurs no licensing costs, expenses may arise from hosting, cloud infrastructure, or hiring skilled Apache Airflow engineers to implement and maintain workflows. Upstaff connects you with cost-effective, expert developers to maximize Airflow’s potential without hidden fees.

What are common Apache Airflow interview questions for data engineers? Arrow

When hiring an Apache Airflow developer, expect questions like:

  • How do you create and manage DAGs in Apache Airflow?

  • Can you explain the role of the Scheduler and Executor in Airflow?

  • How do you handle task retries and error handling in Airflow pipelines?

  • Describe your experience integrating Airflow with cloud platforms like AWS or GCP.

  • How would you optimize an Airflow DAG for a large-scale ETL process?
    These questions assess expertise in building robust data pipelines. Upstaff’s pre-vetted Apache Airflow developers are prepared to excel in these areas, ensuring top performance for your projects.

Why use Upstaff to hire an Apache Airflow engineer? Arrow

Upstaff simplifies the process of hiring expert Apache Airflow engineers by connecting you with pre-vetted professionals skilled in building and optimizing data pipelines for ETL, Data Science, or cloud-based orchestration. Our platform offers access to a global talent pool with deep expertise in Apache Airflow, Python, and integrations with platforms like AWS, GCP, and Snowflake. Unlike generic job boards, Upstaff ensures a rigorous vetting process, matching you with certified developers who deliver tailored solutions for your specific needs, saving you time and ensuring high-quality results. Whether you need a remote Apache Airflow developer for a short-term project or a long-term engagement, Upstaff’s flexible hiring options and focus on technical excellence make it the ideal choice for driving your data workflows forward.